The performance of applications based on natural language processing depends primarily on the environment in which these applications are applied. Intelligent environments will be one of the major applications used to process natural language. The methods for speaker’s gender classification can adapt and improve the performance of natural language processing applications. That is why, this chapter will present an effective speaker’s pitch value detection in noisy environments, which then allows more robust speaker’s gender classification. The chapter presents the algorithm for the speaker’s pitch value detection and performs the comparison in various noisy environments. The experiments are carried out on the part of the publically available Aurora 2 speech database. The results showed that the automatically determined pitch values deviate, on average, only by 8.39 Hz from the reference pitch value. A well-defined pitch value allows a functional speaker’s gender classification. In this chapter, presented speaker’s gender classification works well, even at low signal to noise ratios. The experiments show that the speaker’s gender classification performance at SNR 0 dB is higher than 91% when the automatically determined pitch value is used. Speaker’s gender classification can then be used further in the processes of natural language processing.
Part of the book: Recent Trends in Computational Intelligence