Vowel-non vowel decision using neural networks and rules

Abstract

This paper describes a speaker independent vowel/non-vowel classifier based on neural networks and several rules. RASTA-PLP analysis of the speech signal resulting to mel-cepstral coefficients and a formant tracking method are used in order to provide the feature vectors for the MLP. To train and test the system we used a part of the TIMIT database. The results indicate that the performance of this classifier for speaker independent vowel classification is approximately 98.5% so it can be favorably used for speaker recognition or speech labeling purposes.