A constraint satisfaction model for recognition of stop consonant-vowel (SCV) utterances

We propose a model for recognition of utterances of consonant-vowel (CV) units. The acoustic-phonetic knowledge of the CV classes is incorporated in the form of constraints of a constraint satisfaction model. The model combines evidence from multiple classifiers. The significant feature of this model is that discrimination of the CV units could be enhanced by a combination of even weak evidence derived from the features. The evidence is obtained from multilayer feedforward neural networks trained for subgroups of CV classes. The evidence is enhanced using a set of feedback subnetworks in the constraint satisfaction model. The weights for the connections in the feedback subnetworks are derived using acoustic-phonetic knowledge and the performance statistics of the trained networks. The performance of the proposed model is demonstrated for recognition of utterances of a large number (80) of stop consonant-vowel units for the Indian language Hindi