Vowel-non vowel classification of speech using an MLP and rules

In this paper we present a high precision speaker independent vowel/non vowel classifier based on a simple feed forward MLP (Multi Layer Perceptron) 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 97.25% so it can be favorably used for speaker recognition or speech labeling purposes.