\r\n\tThe eye is our window to the brain. Vision is the ability to interpret and understand the information that comes in through the eyes. The visual system utilizes brain pathways to process and understand what the eyes sense. The dynamic process of vision is to identify, interpret and understand what the eyes see. \r\n\tAn image is a sight which has been recreated. It is an appearance which has been detached from the place and time in which it first made its appearance. Sensing is not the same as seeing. The eyes and the nervous system do the sensing, while the mind does the perceiving.
\r\n
\r\n\t \r\n\tMedical imaging is the process of using technology to view the human body in the interest of diagnosing, monitoring, and treating medical problems. It is especially beneficial when it comes to detecting cancer. Such a threatening disease requires very early detection to improve the chances of survival. Medical imaging is an extremely important element in medical practice in the world of today. While medical knowledge and discernment forms the basis of diagnoses and decisions, medical imaging plays a vital role in confirming any diagnosis. With scientific advancement and a continued effective use, medical imaging will continue to help with earlier detection of health issues and provide increased preventative care.
\r\n
\r\n\tThis book intends to provide readers with a comprehensive overview of the latest and most advanced findings in several aspects of ophthalmic pathology, treatment and surgical strategies, ocular imaging, vision sciences, medical images and perception that focuses on the most important developments in these critically important areas. Enough has been achieved already to make it clear that these fields have enormous possibilities for improving the human health. \r\n\t
",isbn:"978-1-83880-956-0",printIsbn:"978-1-83880-955-3",pdfIsbn:"978-1-83880-957-7",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"2c4e3e515bebe6053f3f1f57e4854462",bookSignature:"Dr. Alireza Ziaei and Prof. Michele Lanza",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10293.jpg",keywords:"Ophthalmology, Physiology, Neuronatomy, Ophthalmic Disease, Ophthalmo-Pathology, Cornea Disease, Retina Disease, Optic Nerve, Vision, Ocular Disease, Eye and Mind, X-Ray",numberOfDownloads:369,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"May 13th 2020",dateEndSecondStepPublish:"September 14th 2020",dateEndThirdStepPublish:"November 13th 2020",dateEndFourthStepPublish:"February 1st 2021",dateEndFifthStepPublish:"April 2nd 2021",remainingDaysToSecondStep:"4 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Dr. Alireza Ziaei, MD has great experience in biomedical research at Eye Research Institute, Massachusetts Eye and Ear, as well as National Center for Image Guided Therapy on radiological cancer detection at Harvard Brigham and Women's Hospital and Surgical Planning Lab.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"271630",title:"Dr.",name:"Alireza",middleName:null,surname:"Ziaei",slug:"alireza-ziaei",fullName:"Alireza Ziaei",profilePictureURL:"https://mts.intechopen.com/storage/users/271630/images/system/271630.jpg",biography:"Dr. Alireza Ziaei, MD is a Physician Scientist at Harvard Medical School in Boston, USA. He is a recipient of numerous awards including National Excellent Researcher Award, National Young Investigator Award, and Science Excellence Prize. His main areas of interest focus on the molecular base of ophthalmic disease and pathology pathways, radiological detection and image guided therapy. He has great experience in biomedical research at Eye Research Institute, Massachusetts Eye and Ear with a focus on corneal and ocular surface diseases, as well as National Center for Image Guided Therapy on radiological cancer detection at Harvard Brigham and Women’s Hospital and Surgical Planning Lab. Dr. Ziaei’s seminal work has been recognized several times. He published and presented several books and numerous articles in highly ranked peer-reviewed journals and conferences worldwide. Dr. Ziaei is serving as an Executive Editor, Editorial Board and Reviewer of reputed journals and scientific societies including Nature, Investigative Ophthalmology & Visual Science (IOVS), American Journal of Ophthalmology (AJO), British Journal of Ophthalmology (BJO), Radiology, Abdominal Radiology, The Journal of Clinical & Experimental Ophthalmology, International Journal of Ophthalmic Pathology (IJOP) and the Journal of Cell Biology: Research & Therapy (CBRT).",institutionString:"Harvard Medical School",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"Harvard Medical School",institutionURL:null,country:{name:"United States of America"}}}],coeditorOne:{id:"240088",title:"Prof.",name:"Michele",middleName:null,surname:"Lanza",slug:"michele-lanza",fullName:"Michele Lanza",profilePictureURL:"https://mts.intechopen.com/storage/users/240088/images/system/240088.jpg",biography:"Michele Lanza was born in Avellino on 28/10/1976. After graduating in Medicine and Surgery at Medical School of Seconda Università di Napoli, he started the residency program in Ophthalmology in 2001. Today he is an Associate Professor in Ophthalmology at Università della Campania, Luigi Vanvitelli. His field of interest are anterior segment disease, keratoconus, glaucoma, corneal distrophies, and cataract. His research field are related to IOL power calculation, eye modification induced by refractive surgery, glaucoma progression, and validation of new diagnostic devices in Ophthalmology.",institutionString:'University of Campania "Luigi Vanvitelli"',position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:'University of Campania "Luigi Vanvitelli"',institutionURL:null,country:{name:"Italy"}}},coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"16",title:"Medicine",slug:"medicine"}],chapters:[{id:"73598",title:"Femto Laser-Assisted Cataract Surgery",slug:"femto-laser-assisted-cataract-surgery",totalDownloads:45,totalCrossrefCites:0,authors:[null]},{id:"73651",title:"Geometric Analysis of Ophthalmic Lens by Backward Method and Optical Simulation",slug:"geometric-analysis-of-ophthalmic-lens-by-backward-method-and-optical-simulation",totalDownloads:58,totalCrossrefCites:0,authors:[null]},{id:"73722",title:"Advances in Non-surgical Treatment Methods in Vision Rehabilitation of Keratoconus Patients",slug:"advances-in-non-surgical-treatment-methods-in-vision-rehabilitation-of-keratoconus-patients",totalDownloads:17,totalCrossrefCites:0,authors:[null]},{id:"74110",title:"Keratoconus Treatment Toolbox: An Update",slug:"keratoconus-treatment-toolbox-an-update",totalDownloads:176,totalCrossrefCites:0,authors:[{id:"291708",title:"Dr.",name:"Vatookarn",surname:"Roongpoovapatr",slug:"vatookarn-roongpoovapatr",fullName:"Vatookarn Roongpoovapatr"},{id:"291711",title:"Dr.",name:"Mohamed",surname:"Abou Shousha",slug:"mohamed-abou-shousha",fullName:"Mohamed Abou Shousha"},{id:"311224",title:"Dr.",name:"Puwat",surname:"Charukamnoetkanok",slug:"puwat-charukamnoetkanok",fullName:"Puwat Charukamnoetkanok"}]},{id:"74107",title:"Acute Hydrops and Its Management",slug:"acute-hydrops-and-its-management",totalDownloads:79,totalCrossrefCites:0,authors:[null]}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"252211",firstName:"Sara",lastName:"Debeuc",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/252211/images/7239_n.png",email:"sara.d@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"63017",title:"Convolutional Neural Networks for Raw Speech Recognition",doi:"10.5772/intechopen.80026",slug:"convolutional-neural-networks-for-raw-speech-recognition",body:'\n
\n
1. Introduction
\n
ASR system has two important tasks—phoneme recognition and whole-word decoding. In ASR, the relationship between the speech signal and phones is established in two different steps [1]. In the first step, useful features are extracted from the speech signal on the basis of prior knowledge. This phase is known as information selection or dimensionality reduction phase. In this, the dimensionality of the speech signal is reduced by selecting the information based on task-specific knowledge. Highly specialized features like MFCC [2] are preferred choice in traditional ASR systems. In the second step, discriminative models estimate the likelihood of each phoneme. In the last, word sequence is recognized using discriminative programming technique. Deep learning system can map the acoustic features into the spoken phonemes directly. A sequence of the phoneme is easily generated from the frames using frame-level classification.
\n
Another side, end-to-end systems perform acoustic frames to phone mapping in one step only. End-to-end training means all the modules are learned simultaneously. Advanced deep learning methods facilitate to train the system in an end-to-end manner. They also have the ability to train the system directly with raw signals, i.e., without hand-crafted features. Therefore, ASR paradigm is shifting from cepstral features like MFCC [2], PLP [3] to discriminative features learned directly from raw speech. End-to-end model may take raw speech signal as input and generates phoneme class conditional probabilities as output. The three major types of end-to-end architectures for ASR are attention-based method, connectionist temporal classification (CTC), and CNN-based direct raw speech model.
\n
Attention-based models directly transcribe the speech into phonemes. Attention-based encoder-decoder uses the recurrent neural network (RNN) to perform sequence-to-sequence mapping without any predefined alignment. In this model, the input sequence is first transformed into a fixed length vector representation, and then decoder maps this fixed length vector into the output sequence. Attention-based encoder-decoder is much capable of learning the mapping between variable-length input and output sequences. Chorowski and Jaitly proposed speaker-independent sequence-to-sequence model and achieved 10.6% WER without separate language models and 6.7% WER with a trigram language model for Wall Street Journal dataset [4]. In attention-based systems, the alignment between the acoustic frame and recognized symbols is performed by attention mechanism, whereas CTC model uses conditional independence assumptions to efficiently solve sequential problems by dynamic programming. Attention model has shown high performance over CTC approach because it uses the history of the target character without any conditional independence assumptions.
\n
Another side, CNN-based acoustic model is proposed by Palaz et al. [5, 6, 7] which processes the raw speech directly as input. This model consists of two stages: feature learning stage, i.e., several convolutional layers, and classifier stage, i.e., fully connected layers. Both the stages are learned jointly by minimizing a cost function based on relative entropy. In this model, the information is extracted by the filters at first convolutional layer and modeled between first and second convolutional layer. In classifier stage, learned features are classified by fully connected layers and softmax layer. This approach claims comparable or better performance than traditional cepstral feature-based system followed by ANN training for phoneme recognition on TIMIT dataset.
\n
This chapter is organized as follows: In Section 2, the work performed in the field of ASR is discussed with the name of related work. Section 3 covers the various architectures of ASR. Section 4 presents the brief introduction about CNN. Section 5 explains CNN-based direct raw speech recognition model. In Section 6, available experimental results are shown. Finally, Section 7 concludes this chapter with the brief discussion.
\n
\n
\n
2. Related work
\n
Traditional ASR system leveraged the GMM/HMM paradigm for acoustic modeling. GMM efficiently processes the vectors of input features and estimates emission probabilities for each HMM state. HMM efficiently normalizes the temporal variability present in speech signal. The combination of HMM and language model is used to estimate the most likely sequence of phones. The discriminative objective function is used to improve the recognition rate of the system by the discriminatively fine-tuned methods [8]. However, GMM has a shortcoming as it shows inability to model the data that is present on the boundary line. Artificial neural networks (ANNs) can learn much better models of data lying on the boundary condition. Deep neural networks (DNNs) as acoustic models tremendously improved the performance of ASR systems [9, 10, 11]. Generally, discriminative power of DNN is used for phoneme recognition and, for decoding task, HMM is preferred choice. DNNs have many hidden layers with a large number of nonlinear units and produce a very large number of outputs. The benefit of this large output layer is that it accommodates the large number of HMM states. DNN architectures have densely connected layers. Therefore, such architectures are more prone to overfitting. Secondly, features having the local correlations become difficult to learn for such architectures. In [12], speech frames are classified into clustered context-dependent states using DNNs. In [13, 14], GMM-free DNN training process is proposed by the researchers. However, GMM-free process demands iterative procedures like decision trees, generating forced alignments. DNN-based acoustic models are gaining much popularity in large vocabulary speech recognition task [10], but components like HMM and n-gram language model are same as in their predecessors.
\n
GMM or DNN-based ASR systems perform the task in three steps: feature extraction, classification, and decoding. It is shown in Figure 1. Firstly, the short-term signal \n\n\ns\nt\n\n\n is processed at time “t” to extract the features \n\n\nx\nt\n\n\n. These features are provided as input to GMM or DNN acoustic model which estimates the class conditional probabilities \n\n\nP\ne\n\n\ni\n\nx\ni\n\n\n\n for each phone class \n\ni\n∈\n\n1\n…\nI\n\n.\n\n The emission probabilities are as follows:
General framework of automatic speech recognition system.
\n
The prior class probability \n\np\n\ni\n\n\n is computed by counting on the training set.
\n
DNN is a feed-forward NN containing multiple hidden layers with a large number of hidden units. DNNs are trained using the back-propagation methods then discriminatively fine-tuned for reducing the gap between the desired output and actual output. DNN-/HMM-based hybrid systems are the effective models which use a tri-phone HMM model and an n-gram language model [10, 15]. Traditional DNN/HMM hybrid systems have several independent components that are trained separately like an acoustic model, pronunciation model, and language model. In the hybrid model, the speech recognition task is factorized into several independent subtasks. Each subtask is independently handled by a separate module which simplifies the objective. The classification task is much simpler in HMM-based models as compared to classifying the set of variable-length sequences directly. Figure 2 shows the hybrid DNN/HMM phoneme recognition model.
\n
Figure 2.
Hybrid DNN/HMM phoneme recognition.
\n
On the other side, researchers proposed end-to-end ASR systems that directly map the speech into labels without any intermediate components. As the advancements in deep learning, it has become possible to train the system in an end-to-end fashion. The high success rate of deep learning methods in vision task motivates the researchers to focus on classifier step for speech recognition. Such architectures are called deep because they are composed of many layers as compared to classical “shallow” systems. The main goal of end-to-end ASR system is to simplify the conventional module-based ASR system into a single deep learning framework. In earlier systems, divide and conquer approaches are used to optimize each step independently, whereas deep learning approaches have a single architecture that leads to more optimal system. End-to-end speech recognition systems directly map the speech to text without requiring predefined alignment between acoustic frame and characters [16, 17, 18, 19, 20, 21, 22, 23, 24]. These systems are generally divided into three broad categories: attention-based model [19, 20, 21, 22], connectionist temporal classification [16, 17, 18, 25], and CNN-based direct raw speech method [5, 6, 7, 26]. All these models have a capability to address the problem of variable-length input and output sequences.
\n
Attention-based models are gaining much popularity in a variety of tasks like handwriting synthesis [27], machine translation [28], and visual object classification [29]. Attention-based models directly map the acoustic frame into character sequences. However, this model differs from other machine translation tasks by requesting much longer input sequences. This model generates a character based on the inputs and history of the target character. The attention-based models use encoder-decoder architecture to perform the sequence mapping from speech feature sequences to text as shown in Figure 3. Its extension, i.e., attention-based recurrent networks, has also been successfully applied to speech recognition. In the noisy environment, these models’ results are poor because the estimated alignment is easily corrupted by noise. Another issue with this model is that it is hard to train from scratch due to misalignment on longer input sequences. Sequence-to-sequence networks have also achieved many breakthroughs in speech recognition [20, 21, 22]. They can be divided into three modules: an encoding module that transforms sequences, attention module that estimates the alignment between the hidden vector and targets, and decoding module that generates the output sequence. To develop successful sequence-to-sequence model, the understanding and preventing limitations are required. The discriminative training is a different way of training that raises the performance of the system. It allows the model to focus on most informative features with the risk of overfitting.
\n
Figure 3.
Attention-based ASR model.
\n
End-to-end trainable speech recognition systems are an important application of attention-based models. The decoder network computes a matching score between hidden states generated by the acoustic encoder network at each input time. It processes its hidden states to form a temporal alignment distribution. This matching score is used to estimate the corresponding encoder states. The difficulty of attention-based mechanism in speech recognition is that the feature inputs and corresponding letter outputs generally proceed in the same order with only small deviations within word. However, the different length of input and output sequences makes it more difficult to track the alignment. The advantage of attention-based mechanism is that any conditional independence assumptions (Markov assumption) are not required in this mechanism. Attention-based approach replaces the HMM with RNN to perform the sequence prediction. Attention mechanism automatically learns alignment between the input features and desired character sequence.
\n
CTC techniques infer the speech-label alignment automatically. CTC [25] was developed for decoding the language. Firstly, Hannun et al. [17] used it for decoding purpose in Baidu’s deep speech network. CTC uses dynamic programming [16] for efficient computation of a strictly monotonic alignment. However, graph-based decoding and language model are required for it. CTC approaches use RNN for feature extraction [28]. Graves et al. [30] used its objective function in deep bidirectional long short-term memory (LSTM) system. This model successfully arranges all possible alignments between input and output sequences during model training, not on the prior.
\n
Two different versions of beam search are adopted by [16, 31] for decoding CTC models. Figure 4 shows the working architecture of the CTC model. In this, noisy and not informative frames are discarded by the introduction of the blank label which results in the optimal output sequence. CTC uses intermediate label representation to identify the blank labels, i.e., no output labels. CTC-based NN model shows high recognition rate for both phoneme recognition [32] and LVCSR [16, 31]. CTC-trained neural network with language model offers excellent results [17].
\n
Figure 4.
CTC model for speech recognition.
\n
End-to-end ASR systems perform well and achieve good results, yet they face two major challenges. First is how to incorporate lexicons and language models into decoding. However, [16, 31, 33] have incorporated lexicons for searching paths. Second, there is no shared experimental platform for the purpose of benchmark. End-to-end systems differ from the traditional system in both aspects: model architecture and decoding methods. Some efforts were also made to model the raw speech signal with little or no preprocessing [34]. Palaz et al. [6] showed in his study that CNN [35] can calculate the class conditional probabilities from raw speech signal as direct input. Therefore, CNNs are the preferred choice to learn features from the raw speech. Two stages of learned feature process are as follows: initially, features are learned by the filters at first convolutional layer, and then learned features are modeled by second and higher-level convolutional layers. An end-to-end phoneme sequence recognizer directly processes the raw speech signal as inputs and produces a phoneme sequence. The end-to-end system is composed of two parts: convolutional neural networks and conditional random field (CRF). CNN is used to perform the feature learning and classification, and CRFs are used for the decoding stage. CRF, ANN, multilayer perceptron, etc. have been successfully used as decoder. The results on TIMIT phone recognition task also confirm that the system effectively learns the features from raw speech and performs better than traditional systems that take cepstral features as input [36]. This model also produces good results for LVCSR [7].
\n
\n
\n
3. Various architectures of ASR
\n
In this section, a brief review on conventional GMM/DNN ASR, attention-based end-to-end ASR, and CTC is given.
\n
\n
3.1. GMM/DNN
\n
ASR system performs sequence mapping of T-length speech sequence features, \n\nX\n=\n\n\n\nX\nt\n\n∈\n\nR\nD\n\n\n\nt\n=\n1\n,\n\n…\n,\nT\n\n\n,\n\n into an N-length word sequence, \n\nW\n=\n\n\n\nw\nn\n\n∈\nυ\n\n\nn\n=\n1\n\n…\nN\n\n\n where \n\n\nX\nt\n\n\n represents the D-dimensional speech feature vector at frame t and \n\n\nw\nn\n\n\n represents the word at position \n\nn\n\n in the vocabulary, \n\nυ\n\n.
\n
The ASR problem is formulated within the Bayesian framework. In this method, an utterance is represented by some sequence of acoustic feature vector \n\nX\n\n, derived from the underlying sequence of words \n\nW\n\n, and the recognition system needs to find the most likely word sequence as given below [37]:
In Eq. (2), the argument of \n\np\n\nW\nX\n\n\n, that is, the word sequence \n\nW\n\n, is found which shows maximum probability for given feature vector, \n\nX\n.\n\n Using Bayes’ rule, it can be written as
where \n\np\n\nX\nW\n\n\n represents the sequence of speech features and it is evaluated with the help of acoustic model. \n\np\n\nW\n\n\n represents the prior knowledge about the sequence of words \n\nW\n\n and it is determined by the language model. However, current ASR systems are based on a hybrid HMM/DNN [38], which is also calculated using Bayes’ theorem and introduces the HMM state sequence S, to factorize \n\np\n\nW\nX\n\n\n into the following three distributions:
where \n\np\n\nX\nS\n\n\n, \n\np\n\nS\nW\n\n\n, and \n\np\n\nW\n\n\n represent acoustic, lexicon, and language models, respectively. Equation (6) is changed into Eq. (7) in a similar way as Eq. (4) is changed into Eq. (5).
\n
\n
3.1.1. Acoustic models \n\np\n\nX\nS\n\n\n\n
\n
\n\n\np\n\nX\nS\n\n\n can be further factorized using a probabilistic chain rule and Markov assumption as follows:
In Eq. (9), framewise likelihood function \n\np\n\n\nx\nt\n\n\ns\nt\n\n\n\n is changed into the framewise posterior distribution \n\n\n\np\n\n\ns\nt\n\n\nx\nt\n\n\n\n\np\n\n\ns\nt\n\n\n\n\n\n which is computed using DNN classifiers by pseudo-likelihood trick [38]. In Eq. (9), Markov assumption is too strong. Therefore, the contexts of input and hidden states are not considered. This issue can be resolved using either the recurrent neural networks (RNNs) or DNNs with long-context features. A framewise state alignment is required to train the framewise posterior which is offered by an HMM/GMM system.
\n
\n
\n
3.1.2. Lexicon model \n\np\n\nS\nW\n\n\n\n
\n
\n\n\np\n\nS\nW\n\n\n can be further factorized using a probabilistic chain rule and Markov assumption (first order) as follows:
An HMM state transition represents this probability. A pronunciation dictionary performs the conversion from \n\nw\n\n to HMM states through phoneme representation.
\n
\n
\n
3.1.3. Language model \n\np\n\nW\n\n\n\n
\n
Similarly, \n\np\n\nW\n\n\n can be factorized using a probabilistic chain rule and Markov assumption ((m-1)th order) as an m-gram model, i.e.,
The issue of Markov assumption is addressed using recurrent neural network language model (RNNLM) [39], but it increases the complexity of decoding process. The combination of RNNLMs and m-gram language model is generally used and it works on a rescoring technique.
\n
\n
\n
\n
3.2. Attention mechanism
\n
The approach based on attention mechanism does not make any Markov assumptions. It directly finds the posterior \n\np\n\nC\nX\n\n,\n\n on the basis of a probabilistic chain rule:
where \n\n\np\natt\n\n\nC\nX\n\n\n represents an attention-based objective function. \n\np\n\n\nc\nl\n\n\n\nc\n1\n\n,\n…\n,\n\nc\n\nl\n−\n1\n\n\n,\nX\n\n\n\n is obtained by
Eq. (15) represents the encoder and Eq. (18) represents the decoder networks. \n\n\na\nlt\n\n\n represents the soft alignment of the hidden vector, \n\n\nh\nt\n\n\n. Here, \n\n\nr\nl\n\n\n represents the weighted letter-wise hidden vector that is computed by weighted summation of hidden vectors. Content-based attention mechanism with or without convolutional features are shown by \n\nContentAttention\n\n.\n\n\n and \n\nLocationAttention\n\n.\n\n\n, respectively.
\n
\n
3.2.1. Encoder network
\n
The input feature vector \n\nX\n\n is converted into a framewise hidden vector, \n\n\nh\nt\n\n\n using Eq. (15). The preferred choice for an encoder network is BLSTM, i.e.,
\n
\n\nEncoder\n\nX\n\n≜\n\nBLSTM\nt\n\n\nX\n\n\nE19
\n
It is to be noted that the computational complexity of the encoder network is reduced by subsampling the outputs [20, 21].
\n\n\ng\n\n represents a learnable parameter. \n\n\n\n\ne\nlt\n\n\n\nt\n=\n1\n\nT\n\n\n represents a T-dimensional vector. \n\ntanh\n\n.\n\n\n and \n\nLin\n\n.\n\n\n represent the hyperbolic tangent activation function and linear layer with learnable matrix parameters, respectively.
\n
\n
\n
3.2.3. Location-aware attention mechanism
\n
It is an extended version of content-based attention mechanism to deal with the location-aware attention. If \n\n\na\n\nl\n−\n1\n\n\n=\n\n\n\na\n\nl\n−\n1\n\n\n\n\nt\n=\n1\n\nT\n\n\n is replaced in Eq. (16), then \n\nLocationAware\n\n.\n\n\n is represented as follows:
Here, * denotes 1-D convolution along the input feature axis, \n\nt\n\n, with the convolution parameter, \n\nR\n\n, to produce the set of T features \n\n\n\n\nf\nt\n\n\n\nt\n=\n1\n\nT\n\n.\n\n
\n
\n
\n
3.2.4. Decoder network
\n
The decoder network is an RNN that is conditioned on previous output \n\n\nC\n\nl\n−\n1\n\n\n\n and hidden vector \n\n\nq\n\nl\n−\n1\n\n\n\n. LSTM is preferred choice of RNN that represented as follows:
\n\n\n\nr\nl\n\n\n represents the concatenated vector of the letter-wise hidden vector; \n\n\nc\n\nl\n−\n1\n\n\n\n represents the output of the previous layer which is taken as input.
\n
\n
\n
3.2.5. Objective function
\n
The objective function of the attention model is computed from the sequence posterior
where \n\n\nc\nl\n∗\n\n\n represents the ground truth of the previous characters. Attention-based approach is a combination of letter-wise objectives based on multiclass classification with the conditional ground truth history \n\n\nc\nl\n∗\n\n,\n…\n,\n\nc\n\nl\n−\n1\n\n∗\n\n\n in each output \n\nl\n\n.
\n
\n
\n
\n
3.3. Connectionist temporal classification (CTC)
\n
The CTC formulation is also based on Bayes’ decision theory. It is to be noted that L-length letter sequence,
In \n\n\nC\n′\n\n\n, \n\n\nc\nl\n′\n\n\n is always \n\n\n\n“\n\n<\nb\n>\n\n\n”\n\n\n and letter when \n\nl\n\n is an odd and an even number, respectively. Similar as DNN/HMM model, framewise letter sequence with an additional blank symbol
Same as Eq. (3), CTC also uses Markov assumption, i.e., \n\np\n\nC\nZ\nX\n\n≈\np\n\nC\nZ\n\n\n, to simplify the dependency of the CTC acoustic model, \n\np\n\nZ\nX\n\n\n, and CTC letter model, \n\np\n\nC\nZ\n\n\n.
\n
\n
3.3.1. CTC acoustic model
\n
Same as DNN/HMM acoustic model, \n\np\n\nZ\nX\n\n\n can be further factorized using a probabilistic chain rule and Markov assumption as follows:
The framewise posterior distribution, \n\np\n\n\nz\nt\n\nX\n\n\n is computed from all inputs, \n\nX\n\n, and it is directly modeled using bidirectional LSTM [30, 40]:
where \n\nSoftmax\n\n.\n\n\n represents the softmax activation function. \n\nLinB\n\n.\n\n\n is used to convert the hidden vector, \n\n\nh\nt\n\n\n, to a \n\n\n\n\nU\n\n+\n1\n\n\n\n dimensional vector with learnable matrix and bias vector parameter. \n\n\nBLSTM\nt\n\n\n.\n\n\n takes full input sequence as input and produces hidden vector \n\n\n\nh\nt\n\n\n\n at t.
\n
\n
\n
3.3.2. CTC letter model
\n
By applying Bayes’ decision theory probabilistic chain rule and Markov assumption, \n\np\n\nZ\nX\n\n\n can be written as
where \n\np\n\n\nz\nt\n\n\n\nz\n\nt\n−\n1\n\n\n,\nC\n)\n\n represents state transition probability. \n\np\n\nC\n\n\n represents letter-based language model, and \n\np\n\nZ\n\n\n represents the state prior probability. CTC architecture incorporates letter-based language model. CTC architecture can also incorporate a word-based language model by using letter-to-word finite state transducer during decoding [18]. The CTC has the monotonic alignment property, i.e.,
\n
when \n\n\nz\n\nt\n−\n1\n\n\n=\n\nc\nm\n′\n\n\n, then \n\n\nz\nt\n\n=\n\nc\nl\n′\n\n\n where \n\nl\n≥\nm\n\n.
\n
Monotonic alignment property is an important constraint for speech recognition, so ASR sequence-to-sequence mapping should follow the monotonic alignment. This property is also satisfied by HMM/DNN.
\n
\n
\n
3.3.3. Objective function
\n
The posterior, \n\np\n\nC\nX\n\n\n, is represented as
Viterbi method and forward-backward algorithm are dynamic programming algorithm which is used to efficiently compute the summation over all possible \n\nZ\n.\n\n CTC objective function \n\n\np\nCTC\n\n\nC\nX\n\n\n is designed by excluding the \n\np\n\nC\n\n/\np\n\nZ\n\n\n from Eq. (23).
\n
The CTC formulation is also same as HMM/DNN. The minute difference is that Bayes’ rule is applied to \n\np\n\nC\nZ\n\n\n instead of \n\np\n\nW\nX\n\n\n. It has also three distribution components like HMM/DNN, i.e., framewise posterior distribution, \n\np\n\n\nz\nt\n\nX\n\n;\n\n transition probability, \n\np\n\n\nz\nt\n\n\n\nz\n\nt\n−\n1\n\n\n,\nC\n\n\n\n; and letter model, \n\np\n\nC\n\n.\n\n It also uses Markov assumption. It does not fully utilize the benefit of end-to-end ASR, but its character output representation still possesses the end-to-end benefits.
\n
\n
\n
\n
\n
4. Convolutional neural networks
\n
CNNs are the popular variants of deep learning that are widely adopted in ASR systems. CNNs have many attractive advancements, i.e., weight sharing, convolutional filters, and pooling. Therefore, CNNs have achieved an impressive performance in ASR. CNNs are composed of multiple convolutional layers. Figure 5 shows the block diagram of CNN. LeCun and Bengio [41] describe the three states of convolutional layer, i.e., convolution, pooling, and nonlinearity.
\n
Figure 5.
Block diagram of convolutional neural network.
\n
Deep CNNs set a new milestone by achieving approximate human level performance through advanced architectures and optimized training [42]. CNNs use nonlinear function to directly process the low-level data. CNNs are capable of learning high-level features with high complexity and abstraction. Pooling is the heart of CNNs that reduces the dimensionality of a feature map. Maxout is widely used nonlinearity and has shown its effectiveness in ASR tasks [43, 44].
\n
Pooling is an important concept that transforms the joint feature representation into the valuable information by keeping the useful information and eliminating insignificant information. Small frequency shifts that are common in speech signal are efficiently handled using pooling. Pooling also helps in reducing the spectral variance present in the input speech. It maps the input from p adjacent units into the output by applying a special function. After the element-wise nonlinearities, the features are passed through pooling layer. This layer executes the downsampling on the feature maps coming from previous layer and produces the new feature maps with a condensed resolution. This layer drastically reduces the spatial dimension of input. It serves the two main purposes. The first is that the amount of parameters or weight is reduced by 65%, thus lessening the computational cost. The second is that it controls the overfitting. This term refers to when a model is so tuned to the training examples.
\n
\n
\n
5. CNN-based end-to-end approach
\n
A novel acoustic model based on CNN is proposed by Palaz et al. [5] which is shown in Figure 6. In this, raw speech signal is segmented into input speech signal \n\n\ns\nt\nc\n\n=\n\n\ns\n\nt\n−\nc\n\n\n…\n\ns\nt\n\n…\n\ns\n\nt\n+\nc\n\n\n\n\n in the context of 2c frames having spanning window \n\n\nw\nin\n\n\n milliseconds. First convolutional layer learns the useful features from the raw speech signal, and remaining convolutional layers further process these features into the useful information. After processing the speech signal, CNN estimates the class conditional probability, i.e., \n\nP\n\n\ni\n/\n\ns\nt\nc\n\n\n\n\n, which is used to calculate emission scaled-likelihood \n\nP\n\n\n\ns\nt\nc\n\n/\ni\n\n\n\n. Several filter stages are present in the network before the classification stage. A filter stage is a combination of convolutional layer, pooling layer, and a nonlinearity. The joint training of feature stage and classifier stage is performed using the back-propagation algorithm.
\n
Figure 6.
CNN-based raw speech phoneme recognition system.
\n
The end-to-end approach employs the following understanding:
Speech signals are non-stationary in nature. Therefore, they are processed in a short-term manner. Traditional feature extraction methods generally use 20–40 ms sliding window size. Although in the end-to-end approach, short-term processing of signal is required. Therefore, the size of the short-term window is taken as hyperparameter which is automatically determined during training.
Feature extraction is a filter operation because its components like Fourier transform, discrete cosine transform, etc. are filtering operations. In traditional systems, filtering is applied on both frequency and time. So, this factor is also considered in building convolutional layer in end-to-end system. Therefore, the number of filter banks and their parameters are taken as hyperparameters that are automatically determined during training.
The short-term processing of speech signal spread the information across time. In traditional systems, this spread information is modeled by calculating temporal derivatives and contextual information. Therefore, intermediate representation is supplied to classifier and calculated by taking long time span of input speech signal. Therefore, \n\n\nw\nin\n\n\n, the size of input window, is taken as hyperparameter, which is estimated during training.
\n
The end-to-end model estimates \n\nP\n\n\ni\n/\n\ns\nt\nc\n\n\n\n\n by processing the speech signal with minimal assumptions or prior knowledge.
\n
\n
\n
6. Experimental results
\n
In this model, a number of hyperparameters are used to specify the structure of the network. The number of hidden units in each hidden layer is very important; hence, it is taken as hyperparameter. \n\n\nw\nin\n\n\n represents the time span of input speech signal. \n\nkW\n\n represents the kernel and temporal window width. \n\ndW\n\n represents the shift of temporal window. \n\n\nkW\nmp\n\n\n represents max-pooling kernel width and \n\n\ndW\nmp\n\n\n represents the shift of max-pooling kernel. The value of all hyperparameters is estimated during training based on frame-level classification accuracy on validation data. The range of hyperparameters after validation is shown in Table 1.
\n
\n
\n
\n
\n\n
\n
Hyperparameter
\n
Units
\n
Range
\n
\n\n\n
\n
Input window size (\n\n\nw\nin\n\n\n)
\n
ms
\n
100–700
\n
\n
\n
Kernel width of the first ConvNet layer (\n\n\nkW\n1\n\n\n)
\n
Samples
\n
10–90
\n
\n
\n
Kernel width of the nth ConvNet layer (\n\n\nkW\nn\n\n\n)
\n
Samples
\n
1–11
\n
\n
\n
Number of filters per kernel (\n\n\nd\nout\n\n\n\nt)
\n
Filters
\n
20–100
\n
\n
\n
Max-pooling kernel width (\n\n\nkW\nmp\n\n\n)
\n
Frames
\n
2–6
\n
\n
\n
Number of hidden units in the classifier
\n
Units
\n
200–1500
\n
\n\n
Table 1.
Range of hyperparameter for TIMIT dataset during validation.
\n
The experiments are conducted for three convolutional layers. The speech window size (\n\n\nw\nin\n\n\n is taken 250 ms with a shift of temporal window \n\n\ndW\n\n\n 10 ms. Table 2 shows the comparison of existing end-to-end speech recognition model in the context of PER. The results of the experiments conducted on TIMIT dataset for this model are compared with already existing techniques, and it is shown in Table 3. The main advantages of this model are that it uses only few parameters and offers better performance. It also increases the generalization capability of the classifiers.
\n
\n
\n
\n\n
\n
End-to-end speech recognition model
\n
PER (%)
\n
\n\n\n
\n
CNN-based speech recognition system using raw speech as input [7]
\n
33.2
\n
\n
\n
Estimating phoneme class conditional probabilities from raw speech signal using convolutional neural networks [36]
\n
32.4
\n
\n
\n
Convolutional neural network-based continuous speech recognition using raw speech signal [6]
\n
32.3
\n
\n
\n
End-to-end phoneme sequence recognition using convolutional neural networks [5]
\n
27.2
\n
\n
\n
CNN-based direct raw speech model
\n
21.9
\n
\n
\n
End-to-end continuous speech recognition using attention-based recurrent NN: First results [19]
\n
18.57
\n
\n
\n
Toward end-to-end speech recognition with deep convolutional neural networks [44]
\n
18.2
\n
\n
\n
Attention-based models for speech recognition [20]
\n
17.6
\n
\n
\n
Segmental recurrent neural networks for end-to-end speech recognition [45]
\n
17.3
\n
\n\n
Table 2.
Comparison of existing end-to-end speech model in the context of PER (%).
Bold value and text represent the performance of the CNN-based direct raw speech model.
Attention-based models for speech recognition [20]
\n
17.6
\n
\n
\n
Segmental recurrent neural networks for end-to-end speech recognition [45]
\n
17.3
\n
\n
\n
Combining time and frequency domain convolution in convolutional neural network-Based phone recognition [47]
\n
16.7
\n
\n
\n
Phone recognition with hierarchical convolutional deep maxout networks [48]
\n
16.5
\n
\n\n
Table 3.
Comparison of existing techniques with CNN-based direct raw speech model in the context of PER (%).
Bold value and text represent the performance of the CNN-based direct raw speech model.
\n
\n
\n
7. Conclusion
\n
This chapter discusses the CNN-based direct raw speech recognition model. This model directly learns the relevant representation from the speech signal in a data-driven manner and calculates the conditional probability for each phoneme class. In this, CNN as an acoustic model consists of a feature stage and classifier stage. Both the stages are trained jointly. Raw speech is supplied as input to first convolutional layer, and it is further processed by several convolutional layers. Classifiers like ANN, CRF, MLP, or fully connected layers calculate the conditional probabilities for each phoneme class. After that decoding is performed using HMM. This model shows the similar performance as shown by MFCC-based conventional mode.
\n
\n\n',keywords:"ASR, attention-based model, connectionist temporal classification, CNN, end-to-end model, raw speech signal",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/63017.pdf",chapterXML:"https://mts.intechopen.com/source/xml/63017.xml",downloadPdfUrl:"/chapter/pdf-download/63017",previewPdfUrl:"/chapter/pdf-preview/63017",totalDownloads:1705,totalViews:1990,totalCrossrefCites:3,totalDimensionsCites:4,hasAltmetrics:0,dateSubmitted:"April 24th 2018",dateReviewed:"July 6th 2018",datePrePublished:null,datePublished:"December 12th 2018",dateFinished:null,readingETA:"0",abstract:"State-of-the-art automatic speech recognition (ASR) systems map the speech signal into its corresponding text. Traditional ASR systems are based on Gaussian mixture model. The emergence of deep learning drastically improved the recognition rate of ASR systems. Such systems are replacing traditional ASR systems. These systems can also be trained in end-to-end manner. End-to-end ASR systems are gaining much popularity due to simplified model-building process and abilities to directly map speech into the text without any predefined alignments. Three major types of end-to-end architectures for ASR are attention-based methods, connectionist temporal classification, and convolutional neural network (CNN)-based direct raw speech model. In this chapter, CNN-based acoustic model for raw speech signal is discussed. It establishes the relation between raw speech signal and phones in a data-driven manner. Relevant features and classifier both are jointly learned from the raw speech. Raw speech is processed by first convolutional layer to learn the feature representation. The output of first convolutional layer, that is, intermediate representation, is more discriminative and further processed by rest convolutional layers. This system uses only few parameters and performs better than traditional cepstral feature-based systems. The performance of the system is evaluated for TIMIT and claimed similar performance as MFCC.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/63017",risUrl:"/chapter/ris/63017",book:{slug:"from-natural-to-artificial-intelligence-algorithms-and-applications"},signatures:"Vishal Passricha and Rajesh Kumar Aggarwal",authors:[{id:"256038",title:"Prof.",name:"Rajesh",middleName:null,surname:"Aggarwal",fullName:"Rajesh Aggarwal",slug:"rajesh-aggarwal",email:"rka15969@gmail.com",position:null,institution:null},{id:"256039",title:"Mr.",name:"Vishal",middleName:null,surname:"Passricha",fullName:"Vishal Passricha",slug:"vishal-passricha",email:"vishal_pasricha@yahoo.com",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Related work",level:"1"},{id:"sec_3",title:"3. Various architectures of ASR",level:"1"},{id:"sec_3_2",title:"3.1. GMM/DNN",level:"2"},{id:"sec_3_3",title:"3.1.1. Acoustic models \n\np\n\nX\nS\n\n\n\n",level:"3"},{id:"sec_4_3",title:"3.1.2. Lexicon model \n\np\n\nS\nW\n\n\n\n",level:"3"},{id:"sec_5_3",title:"3.1.3. Language model \n\np\n\nW\n\n\n\n",level:"3"},{id:"sec_7_2",title:"3.2. Attention mechanism",level:"2"},{id:"sec_7_3",title:"3.2.1. Encoder network",level:"3"},{id:"sec_8_3",title:"3.2.2. Content-based attention mechanism",level:"3"},{id:"sec_9_3",title:"3.2.3. Location-aware attention mechanism",level:"3"},{id:"sec_10_3",title:"3.2.4. Decoder network",level:"3"},{id:"sec_11_3",title:"3.2.5. Objective function",level:"3"},{id:"sec_13_2",title:"3.3. Connectionist temporal classification (CTC)",level:"2"},{id:"sec_13_3",title:"3.3.1. CTC acoustic model",level:"3"},{id:"sec_14_3",title:"3.3.2. CTC letter model",level:"3"},{id:"sec_15_3",title:"3.3.3. Objective function",level:"3"},{id:"sec_18",title:"4. Convolutional neural networks",level:"1"},{id:"sec_19",title:"5. CNN-based end-to-end approach",level:"1"},{id:"sec_20",title:"6. Experimental results",level:"1"},{id:"sec_21",title:"7. Conclusion",level:"1"}],chapterReferences:[{id:"B1",body:'Rabiner LR, Juang B-H. Fundamentals of Speech Recognition. Englewood Cliffs: PTR Prentice Hall; 1993\n'},{id:"B2",body:'Davis SB, Mermelstein P. Comparison of Parametric Representations for Monosyllabic Word Recognition in Continuously Spoken Sentences. Readings in Speech Recognition. Elsevier; 1990. pp. 65-74\n'},{id:"B3",body:'Hermansky H. Perceptual linear predictive (PLP) analysis of speech. The Journal of the Acoustical Society of America. 1990;87(4):1738-1752\n'},{id:"B4",body:'Chorowski J, Jaitly N. Towards better decoding and language model integration in sequence to sequence models. 2016. arXiv preprint arXiv:161202695\n'},{id:"B5",body:'Palaz D, Collobert R, Doss MM. End-to-end phoneme sequence recognition using convolutional neural networks. 2013. arXiv preprint arXiv:13122137\n'},{id:"B6",body:'Palaz D, Doss MM, Collobert R. Convolutional neural networks-based continuous speech recognition using raw speech signal. In: Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on. IEEE; 2015\n'},{id:"B7",body:'Palaz D, Collobert R. Analysis of CNN-Based Speech Recognition System Using Raw Speech as Input. In Proceeding of Interspeech 2015 (No. EPFL-Conf-210029); 2015\n'},{id:"B8",body:'O’Shaughnessy D. Automatic speech recognition: History, methods and challenges. Pattern Recognition. 2008;41(10):2965-2979\n'},{id:"B9",body:'Dahl GE, Yu D, Deng L, Acero A. Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. IEEE Transactions on Audio, Speech and Language Processing. 2012;20(1):30-42\n'},{id:"B10",body:'Hinton G, Deng L, Yu D, Dahl GE, Mohamed AR, Jaitly N, et al. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. 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IEEE; 2014\n'},{id:"B44",body:'Zhang Y, Pezeshki M, Brakel P, Zhang S, Bengio CLY, Courville A. Towards end-to-end speech recognition with deep convolutional neural networks. 2017. arXiv preprint arXiv:170102720\n'},{id:"B45",body:'Lu L, Kong L, Dyer C, Smith NA, Renals S. Segmental recurrent neural networks for end-to-end speech recognition. In: INTERSPEECH 2016; 8 September 2016. ISCA; 2016\n'},{id:"B46",body:'Fauziya F, Nijhawan G. A Comparative study of phoneme recognition using GMM-HMM and ANN based acoustic modeling. International Journal of Computer Applications. 2014:12-16\n'},{id:"B47",body:'Toth L. Combining time- and frequency-domain convolution in convolutional neural network-based phone recognition. In: Acoustics, Speech and Signal Processing (ICASSP). 2014 IEEE International Conference on May 4 2014. IEEE; pp. 190-194\n'},{id:"B48",body:'Toth L. Phone recognition with hierarchical convolutional deep maxout networks. EURASIP Journal on Audio, Speech, and Music Processing. 2015;2015(1):25\n'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Vishal Passricha",address:null,affiliation:'
National Institute of Technology, Kurukshetra, India
National Institute of Technology, Kurukshetra, India
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1. Introduction
Blue green algae are present abundantly in rice fields and are important in helping to maintain rice fields fertility through nitrogen fixation. They are belongs to a group of ubiquitous photosynthetic prokaryotes which possessing the ability to synthesis Chlorophyll a and carryout an important role in nutrient recycling and the maintenance of organic matter in aquatic systems including lakes, rivers and wetland. Nitrogen fixing blue green algae are known to be a prominent component of the microbial population in wetland soils, especially rice fields, contributing significantly to the fertility as a natural bio-fertilizer.
Nitrogen fixation is one of the most important biological processes and, though, the atmosphere contains about 79% nitrogen, most of the plants cannot utilize it. They can utilize combined nitrogen, like ammonium, nitrate, nitrite; etc. This process is called biological nitrogen fixation.
Rice (Oryza sativa) is monocot plant, of the grass family (Poaceae). As a cereal grain, it is the most popular cereal worldwide, serving as a stable food for 39 countries and nearly half of the world’s population [1]. Globally rice is considered as dietary energy source providing 22% of total energy intake [2]. Rice is second highest worldwide produce and consumed stable food and increasing ratio of population demands more production of rice to meet its consumption [3].
Blue green algal species that thrived in rice field release small quantities of ammonia as the major fertilizing product, and small nitrogenous polypeptides during active growth, whereas most of the fixed products are made available mainly through autolysis and decomposition. They have an important role to play in crop production as promising biofertilizers. Here an attempt was made to study the different formulations of blue green algae from the paddy field with the following objectives: Isolation and mass culturing of blue green algae form the areas of selective southern districts of Tamil Nadu. The selective isolated blue green algae have been formulated with different adsorbent like alluvial soil, sand, charcoal, powdered paddy straw and analyzed the interaction effect of various for BGA on vegetative growth of paddy plant (Figure 1).
Figure 1.
Effect of different formulations of mixed blue green algae on paddy plants under greenhouse condition.
2. Materials and methods
2.1 Sampling
The soil samples collected from the areas namely as Thiruvadanai, of Ramnad, Selugai and Amaravathipudur of Sivagangai and Sakkimangalam of Madurai (Figure 2). They were stored at room temperature and were used as samples for further research.
Figure 2.
Sampling sites.
2.2 Culture techniques
The BG 11 with nitrate and without nitrate medium was prepared and sterilized in autoclave for 121°C, 15 lb pressure for 20 min. After cooling, the samples were inoculated in the BG 11 medium for enrichment. The inoculated flasks were maintained at a temperature of 25°C and 12 h light and 12 h darkness (light intensity 3000 lux).
2.3 Identifying and sub culturing
The blue green algal growth was observed and identifying the organisms under Labomed vision 2000 smart scope B6. The selective identified organisms were sub cultured in BG0 under lab and maintained for further analysis (Figure 3).
Figure 3.
Sub culturing of isolated blue green algae.
2.4 Formulations of BGA
The BGA mixture (10 ml of each Microcoleus, Microcystis, Phormidium and Gloecapsa) was added with 50 g of selective adsorbents (alluvial soil, sand, charcoal, powdered paddy straw). Then such combinations were shade dried under laboratory condition. After drying, such mixture was packed in polythene bags further study.
3. Paddy plant selected for general greenhouse procedure
Seeds of Paddy variety CR-1009 were surface sterilized with hot water for 5 min and washed with sterile water repeatedly. Then these seeds were placed in hot water for 10 min to soften the seed coat. Sterile garden soil was used to fill the earthen pots 15 cm height; 52 cm diameter. About 5 kg of sterile soil were taken in each earthen pot which was mixed with different adsorbent formulated BGA. Seeds (15 Nos.) were sown in each pot and germinated seedlings were thinned out to 10 in each pot. The above experimental plants were maintained under greenhouse conditions. The sterilized tap water was used for irrigating the plants. Such experimental pots were assigned for the following treatments:
C—control (without organism)
T1—alluvial soil + mixed BGA
T2—sand + mixed BGA
T3—charcoal + mixed BGA
T4—powdered paddy straw + mixed BGA
3.1 Determination of growth
The paddy plant vegetative growth (15th day) was measured with the following growth parameters.
3.2 Determination of fresh and dry weight
The plant materials were cut into bits and weighed. Then they were dried in an oven at 90°C until the weight became constant.
3.3 Shoot and root length determination
The shoot and root lengths of the plants were measured using a meter-scale.
3.4 Determination of leaf number
The number of leaves or leaflets was counted for each plant.
3.5 Estimation of chlorophyll
The experimental leaf tissue was estimated for chlorophyll by following the method of Arnon [4]. Fifty milligram of Leaf tissue was homogenized in 80% pre chilled acetone by using a mortar and pestle and centrifuged at 3000 rpm. The pellet was homogenized again with acetone and was centrifuged repeatedly till the pellet become pale. The collected supernatants were pooled and the absorbance of the supernatant was read at 645 and 663 nm.
The chlorophyll content (mg/g fr.wt) was calculated by using the following formula:
where l is the path of light length in cm (1 cm), V is the volume of the extract in ml and W is the fresh weight of the sample in g (Chlorophyll contents were expressed either as mg or μg for the plant samples).
3.6 Protein estimation
The experimental fresh leaf tissue of the protein content was estimated by Lowry’s method [5]. About 50 mg of the leaf tissue was weighed and was homogenized in hot 80% ethanol and macerate in a mortar with pestle. The supernatant was discarded and the pellet was collected for the analysis purpose. The collected pellet was suspended in a suitable volume of 5% TCA in an ice-bath for 15 min. The pellet was re extracted once in hot absolute ethanol and twice with ethanol-ether mixture, every time discarding the supernatants after centrifugation. Such collected pellet contained proteins and nucleic acids.
The extracted protein sample was placed in 1 ml of sodium hydroxide at 100°C for 4–5 min. The alkaline copper reagent (5 ml) was added and allowed to stand at room temperature for 10 min. Then the folin phenol reagent (0.5 ml) was added rapidly and mixed immediately. After 30 min, the absorbance was measured at 750 nm in a UV–Visible Spectrophotometer. The quantity of protein in the sample was calculated with a standard curve prepared using bovine serum albumin of different concentrations.
3.7 Statistical analysis
The data collected in this study was subjected to statistical methods standard deviation bar charts and pie charts applied [6].
4. Results and discussion
Blue green algae (cyanobacteria) play an important role in maintenance and build-up of soil fertility, consequently increasing rice growth and yield as a natural biofertilizer [7]. They are photosynthetic nitrogen fixers and are free living. Increase in water-holding capacity through their jelly structure [8].
Cyanobacteria are known to be one of the promising supplements to nitrogenous fertilizer, but the process biological nitrogen fixation, mediated through the enzyme nitrogenase may be inhibited in presence readily available nitrogen source. Supplementation of chemical fertilizer with blue green algae could conserve up to 30% of commercial fertilizer and it is generally believed that the nitrogen fixed by these organisms is made available to the rice plants through exudation or autolysis and microbial decomposition. Onkar et al. [9] in addition to contributing fixed nitrogen and adding organic matter to soil such blue green algae are also known to excrete growth promoting substances, solubilize insoluble phosphates, improve fertilizer use efficiency of crop plants and amend the physical and chemical properties of soils, increasing soil aggregate size, there by correcting soil compaction, reduce oxidizable matter of the soil and narrowing down the C:N ratio [10].
Nitrogen fixing filamentous cyanobacteria occurs in wide range of habitats mainly rice-field ecosystem and agricultural fields [11, 12]. In rice field among photosynthetic aquatic organisms, investigations have been emphasized more on isolation and identification of nitrogen fixing cyanobacterial populations in agro-ecosystems for sustainable agriculture.
Shelf-life of cyanobacteria biofertilizer can be augmented by selecting translucent packing material, dry mixing and paddy straw as a carrier [13]. Conventionally, soil has been used as a carrier for cyanobacterial biofertilizers whereas in one study it was reported that soil based inoculums have proved to be disadvantages due to poor inoculums loading, heavy contamination and its bulky nature [14, 15, 16]. Sugar cane waste; rice husk [17] and coconut coir [18] was developed as new carrier material [13]. Field trials conducted using straw based, soil based and multani mitti based BGA biofertilizer and it was reported that multani mitti based biofertilizer gave highest yield followed by straw based and soil based BGA inoculants [19].
In the present study the paddy field soil was collected from four different villages namely as Thiruvadanai of Ramnad, Selugai and Amaravathipudur of Sivagangai and Sakkimangalam of Madurai district and blue green algae were isolated as Microcoleus, Microcystis, Phormidium and Gloecapsa (Figure 4). These isolates were mixed and formulated in four different adsorbents—alluvial soil, sand, charcoal, and powdered paddy straw. The efficiency of such formulates blue green algae mixture on the morphological and physiological activity of paddy plant (15th day growth) was analyzed (Table 1). According to this all the formulated BGA (blue green algae) inoculated paddy plant showed progressive increase in shoot and root length, fresh and dry weight, number of leaves, chlorophyll and protein content when compared to control plant. Among these formulations the alluvial soil + BGA treated plants showed better growth by means of increase in chlorophyll and protein content which indicated that the photosynthetic and metabolic activity was enhanced due to this treatment. Blue green algae formulated with adsorbents influenced the paddy plant growth and also they contributed to improve the nitrogen fertility in soil.
Figure 4.
Microscopic view of isolated blue green algae from soil samples of sampling paddy fields. (a) Microcoleus, (b) Microcystis, (c) Phormidium, and (d) Gloecapsa.
Growth parameters
Control
T1
T2
T3
T4
Shoot length (cm)
13.17 ± 0.29
18.83 ± 0.29
15.5 ± 0.50
17.43 ± 0.40
18.13 ± 0.12
Root length (cm)
2.13 ± 0.23
3.93 ± 0.12
3.37 ± 0.12
3.6 ± 0.10
3.87 ± 0.12
No of leaves
2
3
2
3
3
Fresh weight (g)
0.17 ± 0.00
0.21 ± 0.00
0.20 ± 0.00
0.207 ± 0.00
0.209 ± 0.001
Dry weight (g)
0.043 ± 0.00
0.052 ± 0.00
0.0507 ± 0.00
0.043 ± 0.00
0.051 ± 0.00
Chlorophyll a (μg)
0.0313 ± 0.001
0.485 ± 0.001
0.251 ± 0.002
0.388 ± 0.001
0.279 ± 0.001
Chlorophyll b (μg)
0.0187 ± 0.001
0.1513 ± 0.001
0.074 ± 0.002
0.104 ± 0.001
0.080 ± 0.001
Table 1.
Effect of different formulations of mixed blue green algae on the growth of Paddy plants under greenhouse condition.
Values are mean of three replicates ± SD.
The shoot and root length and fresh and dry weight of the paddy plant treated with alluvial soil + Mixed BGA and powdered paddy straw+ Mixed BGA showed maximum (18.83 ± 0.29; 3.93 ± 0.12 cm and 0.21 ± 0.00; 0.052 ± 0.00 g & 18.13 ± 0.12; 3.87 ± 0.12 cm and 0.209 ± 0.001; 0.051 ± 0.00 g) growth when compared to control (13.17 ± 0.29; 2.13 ± 0.23 cm and 0.17 ± 0.00; 0.043 ± 0.00). The number of leaves in all treated plants including control was more or less same (2 or 3). But the chlorophyll a, b and total chlorophyll content was higher in (0.485 ± 0.001; 0.1513 ± 0.001; 0.1803 ± 0.001 μg) alluvial soil + Mixed BGA and charcoal + mixed BGA (0.388 ± 0.001; 0.104 ± 0.001; 0.140 ± 0.000 μg) compared to control plant (0.0313 ± 0.001; 0.0187 ± 0.001; 0.0377 ± 0.001 μg) (Table 1 and Figure 5). The other formulated BGA treated plants showed minimal chlorophyll contents. The protein content of treated paddy plant with alluvial soil (28%; 2.52 ± 0.02 mg) + Mixed BGA and charcoal + mixed BGA (29%; 2.52 ± 0.00 mg) was significantly maximum when compare the control (10%) paddy plant (0.873 ± 0.06 mg) (Figure 6).
Figure 5.
Effect of different formulations of mixed blue green algae on the total chlorophyll (μg) content of Paddy plants under greenhouse condition. C, control (without organism); T1, alluvial soil + mixed BGA; T2, sand + mixed BGA; T3, charcoal + mixed BGA; T4, powdered paddy straw + mixed BGA.
Figure 6.
Effect of different formulations of mixed blue green algae on the protein content (mg) of Paddy plants under greenhouse condition. C, control (without organism); T1, alluvial soil + mixed BGA; T2, sand + mixed BGA; T3, charcoal + mixed BGA; T4, powdered paddy straw + mixed BGA.
Katoh et al. [20] reported that Nostoc species are very useful in agricultural applications because of their nitrogen fixation activity, extracellular polysaccharide, photosynthetic system, and particularly desiccation tolerance ability and these properties help to improve the quality of nutrient poor soils. Wetland rice fields could provide an ideal condition for the growth of cyanobacteria, fixing 25–30 kg N ha−1 crop−1, and reducing the use of urea fertilizer in rice culture by 30% [21, 22]. Algalization of BGA in rice cultivation promotes organic forming without usage of chemical fertilizers and production of organic basmati rice has been reported to develop a potential export market in the country [23].
Cyanobacteria also improve soil characteristics by modifying texture size and subsequent aeration and enhancing carbon content and water holding capacity [24]. Such organisms are one of the major components of the nitrogen fixing biomass in paddy fields. The importance of cyanobacteria in agriculture for paddy cultivation is directly proportional to their ability to fix nitrogen and other positive effects for plants and soil. The nitrogen is the second limiting factor next to the water for plant growth in many fields and efficiency of this element is met by fertilizer [25].
Current study suggested that the efficiency of paddy plant growth was enhanced due to the application of formulated BGA with various adsorbents. Such blue green algae were generally applied as biofertilizers in agriculture for improving the soil fertility by the process of biological nitrogen fixation.
5. Conclusion
The blue green algae distributed in different environments. They are actively involved in the fixation of atmospheric nitrogen by the action of nitrogenase enzyme which is present in such organisms but not in plant cells. Microcoleus, Microcystis, Phormidium and Gloecapsa. were isolated from the paddy fields of Thiruvadanai, Selugai, Amaravathipudur, Sakkimangalam areas of Ramnad, Sivagangai and Madurai district. The isolated organisms were mass cultured under laboratory condition and mixed well. The BGA mixture formulated with alluvial soil, sand, charcoal and powdered paddy straw were treated on paddy plant showed significant growth compared to control plant. The present study concluded that the alluvial soil and powdered paddy straw formulated BGA promoted the plant growth by means of enhance the morphological growth but chlorophyll and protein content of the alluvial soil and charcoal formulated BGA treated plant showed was maximum. This indicated that the formulated BGA enhanced morphological and photosynthetic efficiency of the paddy plant under greenhouse condition. The application of such bio-mixture in agriculture for crop production not only increase crop yield which may maintain our environment eco-friendly.
Acknowledgments
The authors have expressed their sincere thanks to the President, Vice-President, Secretary, Principal, Head of the Department, Thiagarajar College, Madurai, Tamil Nadu, India for their encouragement, support and provided the necessary facilities for the successful completion of the research work. And also they expressed their sincere thanks to their family and friends for the successful supportive work.
\n',keywords:"BGA formulations, adsorbents, cyanobacteria, nitrogen fixers, natural biofertilizers",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/72566.pdf",chapterXML:"https://mts.intechopen.com/source/xml/72566.xml",downloadPdfUrl:"/chapter/pdf-download/72566",previewPdfUrl:"/chapter/pdf-preview/72566",totalDownloads:50,totalViews:0,totalCrossrefCites:0,dateSubmitted:"November 29th 2019",dateReviewed:"May 14th 2020",datePrePublished:"June 22nd 2020",datePublished:null,dateFinished:null,readingETA:"0",abstract:"Blue green algae (BGA) are prokaryotic phototrophic organisms that can fix the atmospheric nitrogen biologically, and were directly applied as a biofertilizers in agricultural fields specifically Paddy field. Since they are having the ability to fix nitrogen, they are formulated with various adsorbents for the purpose of enhancing the crop growth along with maintaining the soil fertility and other soil factors responsible for productivity. The present study revealed that the formulations of blue green algae isolated from paddy fields of southern districts with different adsorbents like alluvial soil, sand, charcoal, and powdered paddy straw. All the adsorbents mixed with blue green algae showed significant growth when compared to the control plant. This determined that the adsorbent formulated mixed blue green algae enhanced the paddy plant growth under greenhouse condition.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/72566",risUrl:"/chapter/ris/72566",signatures:"Bagampriyal Selvaraj and Sadhana Balasubramanian",book:{id:"9685",title:"Agroecosystems",subtitle:null,fullTitle:"Agroecosystems",slug:null,publishedDate:null,bookSignature:"Dr. Marcelo L. Larramendy and Dr. Sonia Soloneski",coverURL:"https://cdn.intechopen.com/books/images_new/9685.jpg",licenceType:"CC BY 3.0",editedByType:null,editors:[{id:"14764",title:"Dr.",name:"Marcelo L.",middleName:null,surname:"Larramendy",slug:"marcelo-l.-larramendy",fullName:"Marcelo L. Larramendy"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Materials and methods",level:"1"},{id:"sec_2_2",title:"2.1 Sampling",level:"2"},{id:"sec_3_2",title:"2.2 Culture techniques",level:"2"},{id:"sec_4_2",title:"2.3 Identifying and sub culturing",level:"2"},{id:"sec_5_2",title:"2.4 Formulations of BGA",level:"2"},{id:"sec_7",title:"3. Paddy plant selected for general greenhouse procedure",level:"1"},{id:"sec_7_2",title:"3.1 Determination of growth",level:"2"},{id:"sec_8_2",title:"3.2 Determination of fresh and dry weight",level:"2"},{id:"sec_9_2",title:"3.3 Shoot and root length determination",level:"2"},{id:"sec_10_2",title:"3.4 Determination of leaf number",level:"2"},{id:"sec_11_2",title:"3.5 Estimation of chlorophyll",level:"2"},{id:"sec_12_2",title:"3.6 Protein estimation",level:"2"},{id:"sec_13_2",title:"3.7 Statistical analysis",level:"2"},{id:"sec_15",title:"4. Results and discussion",level:"1"},{id:"sec_16",title:"5. Conclusion",level:"1"},{id:"sec_17",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'Juliana BO. Rice in Human Nutrition. Rome: The International Rice Research Institute and Food Agriculture Organization of the United Nations; 1993'},{id:"B2",body:'Kainuma K. Rice—Its potential to prevent global hunger. In: Proceedings of the Workshop on Suitable Use of Agricultural Resources and Environment Management with Focus on the Role of Rice Farming. Japan: FAO Association; 2004. pp. 41-46'},{id:"B3",body:'Kubo M, Purevdorj M. The future of rice production and consumption. Journal of Food Distribution Research. 2004;35(1):128-142'},{id:"B4",body:'Arnon DI. Copper enzymes in isolated chloroplasts. Polyphenol oxidase in Beta vulgaris. Plant Physiology. 1949;24:1-15'},{id:"B5",body:'Lowry OH, Rosebrough NJ, Farr AL, Randall RJ. Protein measurement with Folin phenol reagent. The Journal of Biological Chemistry. 1951;193:265'},{id:"B6",body:'Little TM, Hills FC. Agricultural Experimentation. USA: John Wiley and Sons; 1978'},{id:"B7",body:'Song T, Martensson L, Eriksson T, Zheng W, Rasmussen U. Biodiversity and seasonal variation of cyanobacterial assemblage in a rice paddy field in Fujian, China. The Federation of European Materials Societies Microbiology Ecology. 2005;54:131-140'},{id:"B8",body:'Roger GA, Bergman B, Henriksson E, Udris M. Utilisation of blue green algae as bio fertilizers. Plant and Soil. 1982;52(1):99-107'},{id:"B9",body:'Onkar NT, Tingujam I, Keithellakpam OS, Oinam AS, Gunapathi O, Laxmipriya K, et al. Enumeration, pigments analysis and nitrogenise activity of Cyanobacteria isolated from unexplored rice fields of Manipur, India falling under Indo-Burma biodiversity hotspots. International Journal of Current Microbiology and Applied Sciences. 2015;4(6):666-680'},{id:"B10",body:'Swarnalakshmi K, Dhar DW, Singh PK. Evaluation of blue green algal inoculation on specific soil parameters. Acta Agronomica Hungarica. 2007;55(3):307-313'},{id:"B11",body:'Chunleuchanon S, Sooksawang A, Teacemroong N, Boonkered N. Diversity of nitrogen fixing cyanobacteria under various ecosystems of Thailand population dynamics as affected by environmental factors. World Journal of Microbiology and Biotechnology. 2003;19:167-173'},{id:"B12",body:'Dhar DW, Prasanna R, Singh BV. Comparative performance of three carrier based blue green algal biofertilizerfor sustainable rice cultivation. Journal of Sustainable Agriculture. 2007;30(2):41-50'},{id:"B13",body:'Jha MN, Prasad AN. Useful carrier for cyanobacteria their response to cyanobacterial growth, acetylene-reductase activity, cyanobacterial grazers and paddy yield in calcareous soil. World Journal of Microbiology. 2005;21:1521-1527'},{id:"B14",body:'Bisoyi RN, Singh PK. Effect of phosphorus fertilization on blue-green algal inoculum production and nitrogen yield under field condition. Biology and Fertility of Soils. 1988;5:338-343'},{id:"B15",body:'Jha MN, Prasad AN, Sharma SG, Bharati RC. Effects of fertilization rate and crop rotation on diazotrophic cyanobacteria in paddy field. World Journal of Microbiology and Biotechnology. 2004;17:463-468'},{id:"B16",body:'Raynand PA, Metting B. Colonization potential of cyanobacteria on temperate Irrigated soils of Washington State, USA. Biological Agriculture and Horticulture. 1988;5(3):197-208'},{id:"B17",body:'Kannaiyan S. Biological fertilizer for sustainable rice (Oryza sativa) production. Advances in Agricultural Research in India. 2000;13:67-107'},{id:"B18",body:'Malliga P, Uma L, Subramanian G. Lignolytic activity of the cyanobacterium Anabaena azollae ML 2 and the value of coir waste as a carrier for BGA biofertilizer. Microbios. 1996;86:175-183'},{id:"B19",body:'Pabbi S. Cyanobacteria biofertiliser (Reveiw). Journal of Eco-friendly Agriculture. 2008;3(2):95-111'},{id:"B20",body:'Katoh H, Furukawa J, Yokotani KT, Yasuaki K. Isolation and purification of an axenic diazotrophic drought tolerant cyanobacterium, Nostoc commune, from natural cyanobacteria l crusts and its utilization for field research on soils polluted with radioisotopes. Biochimica et Biophysica Acta (BBA)-Bioenergetics. 2012;1817(8):1499-1505'},{id:"B21",body:'Choudhary ATMA, Kecskes ML, Kennedy LR. Utilization of BNF technology supplementing urea N for sustainable rice production. Journal of Plant Nutrition. 2004;37(10):1627-1647'},{id:"B22",body:'Hashem MA. Problems and prospects of cyanobacterial biofertilizer for rice cultivation. Australian Journal of Plant Physiology. 2001;28(9):881-888'},{id:"B23",body:'Mulbry W, Kondrad S, Pizarro C. Biofertilizers from algal treatment of dairy and swine manure effluents. Journal of Vegetation Science. 2008;12(4):107-125'},{id:"B24",body:'Richert L, Golubic S, Guedes RL, Ratiskol J, Payri C, Guenennec J. Characterisation of exopolysaccharides produced by Cyanobacteria isolated from Polynesian microbial mats. Current Microbiology. 2006;51(6):379-384'},{id:"B25",body:'Malik FR, Ahmed S, Rizki YM. Utilization of lignocellulosic waste for the nitrogenous biofertilizer. Pakistan Journal of Biological Sciences. 2001;4:1217-1220'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Bagampriyal Selvaraj",address:null,affiliation:'
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BKCI is a part of Web of Science Core Collection (WoSCC) and the world’s leading citation index with multidisciplinary content from the top tier international and regional journals, conference proceedings, and books. The Book Citation Index includes over 104,500 editorially selected books, with 10,000 new books added each year. Containing more than 53.2 million cited references, coverage dates back from 2005 to present. The Book Citation Index is multidisciplinary, covering disciplines across the sciences, social sciences, and arts & humanities.
Produced by the Web Of Science group, BIOSIS Previews research database provides researchers with the most current sources of life sciences information, including journals, conferences, patents, books, review articles, and more. Researchers can also access multidisciplinary coverage via specialized indexing such as MeSH disease terms, CAS registry numbers, Sequence Databank Numbers and Major Concepts.
Produced by the Web Of Science group, Zoological Record is the world’s oldest continuing database of animal biology. It is considered the world’s leading taxonomic reference, and with coverage back to 1864, has long acted as the world’s unofficial register of animal names. The broad scope of coverage ranges from biodiversity and the environment to taxonomy and veterinary sciences.
Provides a simple way to search broadly for scholarly literature. Includes peer-reviewed papers, theses, books, abstracts and articles, from academic publishers, professsional societies, preprint repositories, universities and other scholarly organizations. Google Scholar sorts articles by weighing the full text of each article, the author, the publication in which the article appears, and how often the article has been cited in other scholarly literature, so that the most relevant results are returned on the first page.
Microsoft Academic is a project exploring how to assist human conducting scientific research by leveraging machine’s cognitive power in memory, computation, sensing, attention, and endurance. Re-launched in 2016, the tool features an entirely new data structure and search engine using semantic search technologies. The Academic Knowledge API offers information retrieval from the underlying database using REST endpoints for advanced research purposes.
The national library of the United Kingdom includes 150 million manuscripts, maps, newspapers, magazines, prints and drawings, music scores, and patents. Online catalogues, information and exhibitions can be found on its website. The library operates the world's largest document delivery service, providing millions of items a year to national and international customers.
The digital NSK portal is the central gathering place for the digital collections of the National and University Library (NSK) in Croatia. It was established in 2016 to provide access to the Library’s digital and digitized material collections regardless of storage location. The digital NSK portal enables a unified search of digitized material from the NSK Special Collections - books, visual material, maps and music material. From the end of 2019, all thematic portals are available independently: Digital Books, Digitized Manuscripts, Digitized Visual Materials, Digital Music Materials and Digitized Cartographic Materials (established in 2017). Currently available only in Croatian.
The official DOI (digital object identifier) link registration agency for scholarly and professional publications. Crossref operates a cross-publisher citation linking system that allows a researcher to click on a reference citation on one publisher’s platform and link directly to the cited content on another publisher’s platform, subject to the target publisher’s access control practices. This citation-linking network covers millions of articles and other content items from several hundred scholarly and professional publishers.
Dimensions is a next-generation linked research information system that makes it easier to find and access the most relevant information, analyze the academic and broader outcomes of research, and gather insights to inform future strategy. Dimensions delivers an array of search and discovery, analytical, and research management tools, all in a single platform. Developed in collaboration with over 100 leading research organizations around the world, it brings together over 128 million publications, grants, policy, data and metrics for the first time, enabling users to explore over 4 billion connections between them.
The primary aim of DOAB (Directory of Open Access Books) is to increase discoverability of Open Access books. Metadata will be harvestable in order to maximize dissemination, visibility and impact. Aggregators can integrate the records in their commercial services and libraries can integrate the directory into their online catalogues, helping scholars and students to discover the books.
OAPEN is dedicated to open access, peer-reviewed books. OAPEN operates two platforms, the OAPEN Library (www.oapen.org), a central repository for hosting and disseminating OA books, and the Directory of Open Access Books (DOAB, www.doabooks.org), a discovery service for OA books.
OpenAIRE aims at promoting and implementing the directives of the European Commission (EC) and the European Research Council on the promotion and funding of science and research. OpenAIRE supports the Open Access Mandate and the Open Research Data Pilot developed as part of the Horizon 2020 projects.
An integrated information service combining reference databases, subscription management, online journals, books and linking services. Widely used by libraries, schools, government institutions, medical institutions, corporations and others.
SFX® link resolver gives patrons and librarians a wealth of features that optimize management of and access to resources. It provides patrons with a direct route to electronic full-text records through OpenURL linking, delivers alternative links for further resource discovery, access to journals, and more. Released in 2001 as the first OpenURL resolver, SFX is continuously enhanced to support the newest industry developments and meet the evolving needs of customers. The records include a mix of scholarly material – primarily articles and e-books – but also conference proceedings, newspaper articles, and more.
A non-profit, membership, computer library service and research organization dedicated to the public purposes of furthering access to the world's information and reducing information costs. More than 41,555 libraries in 112 countries and territories around the world use OCLC services to locate, acquire, catalogue, lend and preserve library materials.
The world’s largest collection of open access research papers. CORE's mission is to aggregate all open access research outputs from repositories and journals worldwide and make them available to the public. In this way CORE facilitates free unrestricted access to research for all.
Perlego is a digital online library focusing on the delivery of academic, professional and non-fiction eBooks. It is a subscription-based service that offers users unlimited access to these texts for the duration of their subscription, however IntechOpen content integrated on the platform will always be available for free. They have been billed as “the Spotify for Textbooks” by the Evening Standard. Perlego is based in London but is available to users worldwide.
MyScienceWork provides a suite of data-driven solutions for research institutions, scientific publishers and private-sector R&D companies. MyScienceWork's comprehensive database includes more than 90 million scientific publications and 12 million patents.
CNKI (China National Knowledge Infrastructure) is a key national information construction project under the lead of Tsinghua University, and supported by PRC Ministry of Education, PRC Ministry of Science, Propaganda Department of the Communist Party of China and PRC General Administration of Press and Publication. CNKI has built a comprehensive China Integrated Knowledge Resources System, including journals, doctoral dissertations, masters' theses, proceedings, newspapers, yearbooks, statistical yearbooks, ebooks, patents, standards and so on. CNKI keeps integrating new contents and developing new products in 2 aspects: full-text academic resources, software on digitization and knowledge management. Began with academic journals, CNKI has become the largest and mostly-used academic online library in China.
As one of the largest digital content platform in China,independently developed by CNPIEC, CNPeReading positions herself as “One Platform,Vast Content, Global Services”. Through their new cooperation model and service philosophy, CNPeReading provides integrated promotion and marketing solutionsfor upstream publishers, one-stop, triune, recommendation, online reading and management servicesfor downstream institutions & libraries.
ERIC (Education Resources Information Center), sponsored by the Institute of Education Sciences (IES) of the U.S. Department of Education, provides access to education literature to support the use of educational research and information to improve practice in learning, teaching, educational decision-making, and research. The ERIC website is available to the public for searching more than one million citations going back to 1966.
The ACM Digital Library is a research, discovery and networking platform containing: The Full-Text Collection of all ACM publications, including journals, conference proceedings, technical magazines, newsletters and books. A collection of curated and hosted full-text publications from select publishers.
BASE (Bielefeld Academic Search Engine) is one of the world's most voluminous search sengines especially for academic web resources, e.g. journal articles, preprints, digital collections, images / videos or research data. BASE facilitates effective and targeted searches and retrieves high quality, academically relevant results. Other than search engines like Google or Bing BASE searches the deep web as well. The sources which are included in BASE are intellectually selected (by people from the BASE team) and reviewed. That's why data garbage and spam do not occur.
Zentralblatt MATH (zbMATH) is the world’s most comprehensive and longest-running abstracting and reviewing service in pure and applied mathematics. It is edited by the European Mathematical Society (EMS), the Heidelberg Academy of Sciences and Humanities and FIZ Karlsruhe. zbMATH provides easy access to bibliographic data, reviews and abstracts from all areas of pure mathematics as well as applications, in particular to natural sciences, computer science, economics and engineering. It also covers history and philosophy of mathematics and university education. All entries are classified according to the Mathematics Subject Classification Scheme (MSC 2020) and are equipped with keywords in order to characterize their particular content.
IDEAS is the largest bibliographic database dedicated to Economics and available freely on the Internet. Based on RePEc, it indexes over 3,100,000 items of research, including over 2,900,000 that can be downloaded in full text. RePEc (Research Papers in Economics) is a large volunteer effort to enhance the free dissemination of research in Economics which includes bibliographic metadata from over 2,000 participating archives, including all the major publishers and research outlets. IDEAS is just one of several services that use RePEc data.
As the authoritative source for chemical names, structures and CAS Registry Numbers®, the CAS substance collection, CAS REGISTRY®, serves as a universal standard for chemists worldwide. Covering advances in chemistry and related sciences over the last 150 years, the CAS content collection empowers researchers, business leaders, and information professionals around the world with immediate access to the reliable information they need to fuel innovation.
BKCI is a part of Web of Science Core Collection (WoSCC) and the world’s leading citation index with multidisciplinary content from the top tier international and regional journals, conference proceedings, and books. The Book Citation Index includes over 104,500 editorially selected books, with 10,000 new books added each year. Containing more than 53.2 million cited references, coverage dates back from 2005 to present. The Book Citation Index is multidisciplinary, covering disciplines across the sciences, social sciences, and arts & humanities.
Produced by the Web Of Science group, BIOSIS Previews research database provides researchers with the most current sources of life sciences information, including journals, conferences, patents, books, review articles, and more. Researchers can also access multidisciplinary coverage via specialized indexing such as MeSH disease terms, CAS registry numbers, Sequence Databank Numbers and Major Concepts.
Produced by the Web Of Science group, Zoological Record is the world’s oldest continuing database of animal biology. It is considered the world’s leading taxonomic reference, and with coverage back to 1864, has long acted as the world’s unofficial register of animal names. The broad scope of coverage ranges from biodiversity and the environment to taxonomy and veterinary sciences.
Provides a simple way to search broadly for scholarly literature. Includes peer-reviewed papers, theses, books, abstracts and articles, from academic publishers, professsional societies, preprint repositories, universities and other scholarly organizations. Google Scholar sorts articles by weighing the full text of each article, the author, the publication in which the article appears, and how often the article has been cited in other scholarly literature, so that the most relevant results are returned on the first page.
Microsoft Academic is a project exploring how to assist human conducting scientific research by leveraging machine’s cognitive power in memory, computation, sensing, attention, and endurance. Re-launched in 2016, the tool features an entirely new data structure and search engine using semantic search technologies. The Academic Knowledge API offers information retrieval from the underlying database using REST endpoints for advanced research purposes.
The national library of the United Kingdom includes 150 million manuscripts, maps, newspapers, magazines, prints and drawings, music scores, and patents. Online catalogues, information and exhibitions can be found on its website. The library operates the world's largest document delivery service, providing millions of items a year to national and international customers.
The digital NSK portal is the central gathering place for the digital collections of the National and University Library (NSK) in Croatia. It was established in 2016 to provide access to the Library’s digital and digitized material collections regardless of storage location. The digital NSK portal enables a unified search of digitized material from the NSK Special Collections - books, visual material, maps and music material. From the end of 2019, all thematic portals are available independently: Digital Books, Digitized Manuscripts, Digitized Visual Materials, Digital Music Materials and Digitized Cartographic Materials (established in 2017). Currently available only in Croatian.
The official DOI (digital object identifier) link registration agency for scholarly and professional publications. Crossref operates a cross-publisher citation linking system that allows a researcher to click on a reference citation on one publisher’s platform and link directly to the cited content on another publisher’s platform, subject to the target publisher’s access control practices. This citation-linking network covers millions of articles and other content items from several hundred scholarly and professional publishers.
Dimensions is a next-generation linked research information system that makes it easier to find and access the most relevant information, analyze the academic and broader outcomes of research, and gather insights to inform future strategy. Dimensions delivers an array of search and discovery, analytical, and research management tools, all in a single platform. Developed in collaboration with over 100 leading research organizations around the world, it brings together over 128 million publications, grants, policy, data and metrics for the first time, enabling users to explore over 4 billion connections between them.
The primary aim of DOAB (Directory of Open Access Books) is to increase discoverability of Open Access books. Metadata will be harvestable in order to maximize dissemination, visibility and impact. Aggregators can integrate the records in their commercial services and libraries can integrate the directory into their online catalogues, helping scholars and students to discover the books.
OAPEN is dedicated to open access, peer-reviewed books. OAPEN operates two platforms, the OAPEN Library (www.oapen.org), a central repository for hosting and disseminating OA books, and the Directory of Open Access Books (DOAB, www.doabooks.org), a discovery service for OA books.
OpenAIRE aims at promoting and implementing the directives of the European Commission (EC) and the European Research Council on the promotion and funding of science and research. OpenAIRE supports the Open Access Mandate and the Open Research Data Pilot developed as part of the Horizon 2020 projects.
An integrated information service combining reference databases, subscription management, online journals, books and linking services. Widely used by libraries, schools, government institutions, medical institutions, corporations and others.
SFX® link resolver gives patrons and librarians a wealth of features that optimize management of and access to resources. It provides patrons with a direct route to electronic full-text records through OpenURL linking, delivers alternative links for further resource discovery, access to journals, and more. Released in 2001 as the first OpenURL resolver, SFX is continuously enhanced to support the newest industry developments and meet the evolving needs of customers. The records include a mix of scholarly material – primarily articles and e-books – but also conference proceedings, newspaper articles, and more.
A non-profit, membership, computer library service and research organization dedicated to the public purposes of furthering access to the world's information and reducing information costs. More than 41,555 libraries in 112 countries and territories around the world use OCLC services to locate, acquire, catalogue, lend and preserve library materials.
The world’s largest collection of open access research papers. CORE's mission is to aggregate all open access research outputs from repositories and journals worldwide and make them available to the public. In this way CORE facilitates free unrestricted access to research for all.
Perlego is a digital online library focusing on the delivery of academic, professional and non-fiction eBooks. It is a subscription-based service that offers users unlimited access to these texts for the duration of their subscription, however IntechOpen content integrated on the platform will always be available for free. They have been billed as “the Spotify for Textbooks” by the Evening Standard. Perlego is based in London but is available to users worldwide.
MyScienceWork provides a suite of data-driven solutions for research institutions, scientific publishers and private-sector R&D companies. MyScienceWork's comprehensive database includes more than 90 million scientific publications and 12 million patents.
CNKI (China National Knowledge Infrastructure) is a key national information construction project under the lead of Tsinghua University, and supported by PRC Ministry of Education, PRC Ministry of Science, Propaganda Department of the Communist Party of China and PRC General Administration of Press and Publication. CNKI has built a comprehensive China Integrated Knowledge Resources System, including journals, doctoral dissertations, masters' theses, proceedings, newspapers, yearbooks, statistical yearbooks, ebooks, patents, standards and so on. CNKI keeps integrating new contents and developing new products in 2 aspects: full-text academic resources, software on digitization and knowledge management. Began with academic journals, CNKI has become the largest and mostly-used academic online library in China.
As one of the largest digital content platform in China,independently developed by CNPIEC, CNPeReading positions herself as “One Platform,Vast Content, Global Services”. Through their new cooperation model and service philosophy, CNPeReading provides integrated promotion and marketing solutionsfor upstream publishers, one-stop, triune, recommendation, online reading and management servicesfor downstream institutions & libraries.
ERIC (Education Resources Information Center), sponsored by the Institute of Education Sciences (IES) of the U.S. Department of Education, provides access to education literature to support the use of educational research and information to improve practice in learning, teaching, educational decision-making, and research. The ERIC website is available to the public for searching more than one million citations going back to 1966.
The ACM Digital Library is a research, discovery and networking platform containing: The Full-Text Collection of all ACM publications, including journals, conference proceedings, technical magazines, newsletters and books. A collection of curated and hosted full-text publications from select publishers.
BASE (Bielefeld Academic Search Engine) is one of the world's most voluminous search sengines especially for academic web resources, e.g. journal articles, preprints, digital collections, images / videos or research data. BASE facilitates effective and targeted searches and retrieves high quality, academically relevant results. Other than search engines like Google or Bing BASE searches the deep web as well. The sources which are included in BASE are intellectually selected (by people from the BASE team) and reviewed. That's why data garbage and spam do not occur.
Zentralblatt MATH (zbMATH) is the world’s most comprehensive and longest-running abstracting and reviewing service in pure and applied mathematics. It is edited by the European Mathematical Society (EMS), the Heidelberg Academy of Sciences and Humanities and FIZ Karlsruhe. zbMATH provides easy access to bibliographic data, reviews and abstracts from all areas of pure mathematics as well as applications, in particular to natural sciences, computer science, economics and engineering. It also covers history and philosophy of mathematics and university education. All entries are classified according to the Mathematics Subject Classification Scheme (MSC 2020) and are equipped with keywords in order to characterize their particular content.
IDEAS is the largest bibliographic database dedicated to Economics and available freely on the Internet. Based on RePEc, it indexes over 3,100,000 items of research, including over 2,900,000 that can be downloaded in full text. RePEc (Research Papers in Economics) is a large volunteer effort to enhance the free dissemination of research in Economics which includes bibliographic metadata from over 2,000 participating archives, including all the major publishers and research outlets. IDEAS is just one of several services that use RePEc data.
As the authoritative source for chemical names, structures and CAS Registry Numbers®, the CAS substance collection, CAS REGISTRY®, serves as a universal standard for chemists worldwide. Covering advances in chemistry and related sciences over the last 150 years, the CAS content collection empowers researchers, business leaders, and information professionals around the world with immediate access to the reliable information they need to fuel innovation.
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