Mandarin Digits Speech Recognition Using Support Vector Machines

摘要

A method of applying support vector machine (SVM) in speech recognition was proposed, and a speech recognition system for mandarin digits was built up by SVMs. In the system, vectors were linearly extracted from speech feature sequence to make up time-aligned input patterns for SVM, and the decisions of several 2-class SVM classifiers were employed for constructing an N-class classifier. Four kinds of SVM kernel functions were compared in the experiments of speaker-independent speech recognition of mandarin digits. And the kernel of radial basis function has the highest accurate rate of 99.33 percent, which is better than that of the baseline system based on hidden Markov models (HMM) (97.08 percent). And the experiments also show that SVM can outperform HMM especially when the samples for learning were very limited.