Phone classification using HMM/SVM system and normalization technique

Support vector machines (SVM) were originally developed for binary classification and extended for multi-class classification. Due to their powerfulness and adaptation to hard classification problems, we have chosen them for automatic speech recognition (ASR). The aim of this paper is to investigate the use of SVM multi-class classification coupled with HMM for TIMIT phones. SVM requires that all data samples for training and test to have the same features vector size. Due to the variability in length of phone signals even for the same phone, we have used a normalization technique: zero padding and resampling on all data samples to get them have features vector with the same size. After mapping the 61 TIMIT phones in 46 phones and conducting tests using LibSVM and HTK, we have obtained a classification accuracy rate of 91.26% with the hybrid HMM/SVM system and 71.41% with the HMM-based system. These results show that the hybrid HMM/SVM system using the normalization technique overcomes an HMM-based system and improves the recognition accuracy by 19.8%. Therefore, our experiments result encouraged us to use this hybrid system and normalization technique for the next work in the context of spoken dialogue system.