Combining evidence from multiple modular networks for recognition of consonant-vowel units of speech

In this paper, we present a method to combine evidence from multiple classifiers to recognize a large number of subword units of speech using small size training data sets. Grouping criteria based on phonetic description are considered, to build multiple modular networks for recognition of the large number of units. Nonlinear compression of feature vectors is carried out to obtain reduced dimensional patterns, and multiple classifiers are trained separately using the uncompressed feature vectors and compressed feature vectors. Evidence from multiple classifiers at different stages in the recognition system is combined using the sum rule. Effectiveness of the proposed method is demonstrated for recognition of isolated utterances of 145 consonant-vowel units of speech.