Recognition of consonant-vowel (CV) units of speech in a broadcast news corpus using support vector machines

Abstract

This paper addresses the issues in recognition of the large number of subword units of speech using support vector machines (SVMs). In conventional approaches for multi-class pattern recognition using SVMs, learning involves discrimination of each class against all the other classes. We propose a close-class-set discrimination method suitable for large-class-set pattern recognition problems. In the proposed method, learning involves discrimination of each class against a subset of classes confusable with it and included in its close-class-set. We study the effectiveness of the proposed method in reducing the complexity of multi-class pattern recognition systems based on the one-against-the rest and one-against-one approaches. We discuss the effects of symmetry and uniformity in size of the close-class-sets on the performance for these approaches. We present our studies on recognition of 86 frequently occurring Consonant-Vowel units in a continuous speech database of broadcast news.