English Sentence Recognition Based on HMM and Clustering

Xinguang Li, Jiahua Chen, Zhenjiang Li

Cisco School of Informatics, Guangdong University of Foreign Studies, Guangzhou, China.
School of Business Adminstrtion, South China University of Technology, Guangzhou, China.

DOI: 10.4236/ajcm.2013.31005                

Abstract

For English sentences with a large amount of feature data and complex pronunciation changes contrast to words, there are more problems existing in Hidden Markov Model (HMM), such as the computational complexity of the Viterbi algorithm and mixed Gaussian distribution probability. This article explores the segment-mean algorithm for dimensionality reduction of speech feature parameters, the clustering cross-grouping algorithm and the HMM grouping algorithm, which are proposed for the implementation of the speaker-independent English sentence recognition system based on HMM and clustering. The experimental result shows that, compared with the single HMM, it improves not only the recognition rate but also the recognition speed of the system.

Keywords

English Sentence Recognition; HMM; Clustering

Conflicts of Interest

The authors declare no conflicts of interest.

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