Detection of vowel on set points in continuous speech using autoassociative neural network models

Abstract

Detection of vowel onset points (VOPs) is important for spotting subword units in continuous speech. For consonant-vowel (CV) utterances, VOP is the instant at which the consonant part ends and the vowel part begins. Accurate detection of VOPs is important for recognition of CV units in continuous speech. In this paper, we propose an approach for detection of VOPs using autoassociative neural network (AANN) models. A pair of AANN models are trained for each CV class to capture the characteristics of speech signal in the consonant and vowel regions of that class. The trained AANN models are then used to detect VOPs in continuous speech. The results of studies show that the proposed approach leads to significantly less number of spurious hypotheses.