Segmentation of Assamese phonemes using SOM

Abstract

Phonemes are the smallest distinguishable unit of speech signal. Segmentation of phoneme from its word counterpart is a fundamental and crucial part in speech processing since initial phoneme is used to activate words starting with that phoneme. This work describes an Artificial Neural Network (ANN) based algorithm developed for segmentation and classification of consonant phoneme of Assamese language. The algorithm uses weight vectors, obtained by training Self Organizing Map (SOM) with different number of iteration. Segments of different phonemes constituting the word whose LPC samples are used for training are obtained from SOM weights. A two class Probabilistic Neural Network (PNN) trained with clean Assamese phoneme is used to identify phoneme segment. The classification of phoneme segment is performed as per the consonant phoneme structure of Assamese language which consists of six phoneme families. Experimental results establish the superiority of the SOM-based segmentation over the speaker independent phoneme segmentation reported till now including those obtained using Discrete Wavelet Transform (DWT).