Segmentation and classification of vowel phonemes of assamese speech using a hybrid neural framework

Authors:
Mousmita Sarma;Kandarpa Kumar Sarma
Affiliations:
Department of Electronics and Communication Technology, Gauhati University, Assam, Guwahati, India;Department of Electronics and Communication Technology, Gauhati University, Assam, Guwahati, India
Venue:
Applied Computational Intelligence and Soft Computing
Year:
2012

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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.