An SVM based Confidence Measure for Continuous Speech Recognition

Abstract

The use of support vector machines for speech recognition purposes has been limited by the static nature of this classifier. In this paper, a confidence measure has been proposed and evaluated for the speech features vectors sequence. The confidence measure has been successfully extracted for one versus one multi-class SVM classifier from binary classifiers confidence measures and has been optimized to model the temporal variations of speech feature vectors using a Viterbi like decoding. In the decoding procedure, the effects of bigram lingual modeling and acoustic confidences have been balanced to achieve the best result in the continuous speech recognition applications. The experiments have been arranged on TIMIT corpus for a continuous phoneme recognition system. The results reveal 2.6% superior recognition rates comparing with HMM continuous classic speech recognition methods.