Constraint satisfaction model for enhancement of evidence in recognition of consonant-vowel utterances

In this paper, we address the issues in recognition of a large number of subword units of speech with high confusability among several units. Evidence available from the classification models trained with a limited number of training examples may not be strong to correctly recognize the subword units. We present a constraint satisfaction neural network model that can be used to enhance the evidence for a particular unit with the supporting evidence available for a subset of units confusable with that unit. We demonstrate the enhancement of evidence by the proposed model in recognition of utterances of 145 consonant-vowel units.