Reordering network as postprocessor in modular approach-based neural network architecture for recognition of consonant-vowel (CV) utterances

Abstract

Recognition of consonant-vowel (CV) utterances in Indian languages is a challenging task because of the large number of classes and the high conformability among several classes. Modular approach based on artificial neural network models is considered for recognition of CV utterances. in this approach, the large number of classes is divided into subgroups and a separate network is trained for each subgroup Three different grouping criteria are considered and the performance of modular networks based on these antena is studied. An improved performance is obtained by combining evidence from the three modular networks. Because of similarities among several classes, of a test utterance may not always have the strongest evidence. However,  may be among a small set of alternative classes with strong evidence. We propose to train another neural network to further discriminate among these classes and reorder the alternatives. A significant increase in the performance is obtained by using the reordering network as a postprocessor for recognition of isolated utterances of 65 CV classes in Indian languages.