Chinese speech recognition based on a hybrid SVM and HMM architecture

Authors:
Xingxian Luo
Affiliations:
Computer Center, China West Normal University, Nanchong, P.R. China
Venue:
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
Year:
2011

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Abstract

Hidden Markov Model (HMM), which is widely used in acoustic modeling, has powerful dynamic time-series modeling capability; Support Vector Machine (SVM) still has strong classification ability when the training samples are limited. This paper proposes an improved speech recognition algorithm based on a hybrid SVM/HMM architecture. We use the algorithm to extract the speech features and apply the features to the Speech Recognition (SR) interface of Microsoft Speech SDK (SAPI) to improve the interface data type. The experimental results show that the recognition rate increases greatly.