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Applied Computational Intelligence and Soft Computing
Volume 2012 (2012), Article ID 871324, 8 pages
http://dx.doi.org/10.1155/2012/871324
Research Article

Segmentation and Classification of Vowel Phonemes of Assamese Speech Using a Hybrid Neural Framework

Department of Electronics and Communication Technology, Gauhati University, Assam, Guwahati 781014, India

Received 18 April 2012; Accepted 1 October 2012

Academic Editor: F. Morabito

Copyright © 2012 Mousmita Sarma and Kandarpa Kumar Sarma. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

In spoken word recognition, one of the crucial points is to identify the vowel phonemes. This paper describes an Artificial Neural Network (ANN) based algorithm developed for the segmentation and recognition of the vowel phonemes of Assamese language from some words containing those vowels. Self-Organizing Map (SOM) trained with a various number of iterations is used to segment the word into its constituent phonemes. Later, Probabilistic Neural Network (PNN) trained with clean vowel phonemes is used to recognize the vowel segment from the six different SOM segmented phonemes. One of the important aspects of the proposed algorithm is that it proves the validation of the recognized vowel by checking its first formant frequency. The first formant frequency of all the Assamese vowels is predetermined by estimating pole or formant location from the linear prediction (LP) model of the vocal tract. The proposed algorithm shows a high recognition performance in comparison to the conventional Discrete Wavelet Transform (DWT) based segmentation.