Extraction of fixed dimension patterns from varying duration segments of consonant-vowel utterances

Classification models based on multilayer perceptron (MLP) or support vector machine (SVM) have been commonly used for complex pattern classification tasks. These models are suitable for classification of fixed dimension patterns. However, durations of consonant-vowel (CV) utterances vary not only for different classes, but also for a particular CV class. It is necessary to develop a method for representing the CV utterances by patterns of fixed dimension. For CV utterances, vowel onset point (VOP) is the instant at which the consonant part ends and the vowel part begins. Important information necessary for classification of CV utterances is present in the region around the VOP. A segment of fixed duration around the VOP can be processed to extract a pattern of fixed dimension to represent a CV utterance. Accurate detection of vowel onset points is important for recognition of CV utterances of speech. In this paper, we propose an approach for detection of VOP, based on dynamic time alignment between a reference pattern of a CV class and the pattern of an utterance of that class. The results of studies show that the hypothesised VOPs using the proposed approach have less deviation from their actual locations.