Vowel recognition using First Formant Feature

Abstract

Automatic speech recognition (ASR) has made great strides with the development of digital signal processing hardware and software especially using English as the language of choice. In this paper, a new feature extraction method is presented to identify vowels recorded from 80 Malaysian speakers. The features are obtained from Vocal Tract Model based on Bandwidth (BW) approach. Bandwidth approach identifies frequency ranges from the first formant peak of vowel spectrum. Mean magnitudes were calculated from these Bandwidth frequency ranges. Classification results from Bandwidth Approach were then compared with 13 MFCC Coefficients. A BPNN were used to classify the vowels. BW obtained a classification accuracy of 89.58%, which is 0.71% better than MFCC.