Applying conditional random fields on Chinese syllable recognition

Authors:
Jie Li;Xuan Wang;Yi Yang
Affiliations:
Intelligent Computation Research Center, Harbin Institute of Technology, Shenzhen Gradual School;Intelligent Computation Research Center, Harbin Institute of Technology, Shenzhen Gradual School;Intelligent Computation Research Center, Harbin Institute of Technology, Shenzhen Gradual School
Venue:
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Year:
2009

Citing 3
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Abstract

Hidden Markov model (HMM) is successfully used in speech recognition. However, there is an unavoidable flaw in assuming strong independence for sequences labeling in HMM. While Conditional Random Fields (CRFs) can relax this assumption for HMM, and can also solve the label bias problem efficiently. In this paper, we investigate CRFs for Chinese syllable recognition in continuous speech due to its advantages. The experiments show that the syllable label CRF is able to achieve performance comparable to phone-based HMM.