Combining evidence from multiple classifiers for recognition of consonant-vowel units of speech in multiple languages

Abstract

In this paper, we present studies on combining evidence from multiple classifiers to recognize a large number of consonant-vowel (CV) units of speech. Multiple classifier systems may lead to a better solution to the complex speech recognition tasks, when the evidence obtained from individual systems is complementary in nature. Hidden Markov models (HMMs) are based on the maximum likelihood (ML) approach for training CV patterns of variable length. Support vector machine (SVM) models are based on discriminative learning approach for training fixed length CV patterns. Because of the differences in the training methods and in the pattern representation used; they may provide complementary evidence for CV classes. Complementary evidence available from these classifiers is combined using the sum rule. Effectiveness of the multiple classifier system is demonstrated for recognition of CV units of speech in Indian languages.