Spotting consonant-vowel units in continuous speech using alitoassociative neural networks and support vector machines

In this paper, we propose an approach for continuous speech recognition by spotting consonant-vowel (CV) units. The main issues in spotting CV units are the location of anchor points and labelling the regions around these anchor points using suitable classifiers. The vowel onset points (VOPs) have been used as anchor points. The distribution capturing ability of autoassociative neural network (AANN) models is explored for detection of VOPs in continuous speech. We consider support vector machine (SVM) based classifiers due to their ability of generalisation from limited training data and also due to their inherent discriminative learning. The CV spotting approach for continuous speech recognition has been demonstrated for sentences in Indian languages.