Segmentation of Assamese phonemes using hybrid soft-computational approach

Abstract

Phonemes are the smallest distinguishable unit of speech signal. Segmentation of a phoneme from its word counterpart is a fundamental and crucial part in speech processing because an initial phoneme is used to activate words starting with that phoneme. This work describes an artificial neural network-based algorithm developed for segmentation and classification of consonant phoneme of the Assamese language. The algorithm uses weight vectors, obtained by training self-organising map (SOM) with different number of iterations, as a segment of different phonemes constituting the word whose linear prediction coefficients samples are used for training. The algorithm shows an abrupt rise in success rate than the conventional discrete wavelet-based speech segmentation. A two-class probabilistic neural network problem carried out with clean Assamese phoneme is used to identify phoneme segment. The classification of the phoneme segment is alone as per the consonant phoneme structure of the Assamese language which consists of six phoneme families. Experimental results establish the superiority of the SOM-based segmentation over the discrete wavelet transform-based approach.