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Higher Order Spectrum Analysis and Neural Network Classifier for Identification of Speaker in Noisy Environment

The work described in this paper addresses the application of higher order statistics, i e bispectrum or trispectrum, and its cascade neural networks as a feature extraction method, and the use of another neural networks as a classifier in an automatic speech recognition system. The usually used feature extraction method in the early development of the speech recognition system is power spectrum analysis, however, the recognition rate of this system is not high enough, especially when a Gaussian noise is added to the utterance speech data. The K-L transformation (K-LT) technique is then used as a preprocessing element before the neural classifier is utilized, as an effective procedure for orthogonalization and dimensionality reduction of the codebook vectors generated from bispectrum or trispectrum data. Experimental results show that our system could recognize with high recognition rate on the unguided-utterance speech added with different level of Gaussian noises, especially when a higher number of codebook vectors are utilized. It is also shown that the use of PNN shows higher recognition capability cumpare with that of BackPropagation neural system, especially when using unguided utterance speech data added with high level of Gaussian noise.