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English Sentence Recognition Based on HMM and Clustering
Cisco School of Informatics, Guangdong University of Foreign Studies, Guangzhou, China
Cisco School of Informatics, Guangdong University of Foreign Studies, Guangzhou, China
School of Business Adminstrtion, South China University of Technology, Guangzhou, China
- 1 Cisco School of Informatics, Guangdong University of Foreign Studies, Guangzhou, China
- 2 Cisco School of Informatics, Guangdong University of Foreign Studies, Guangzhou, China
- 3 School of Business Adminstrtion, South China University of Technology, Guangzhou, China
American Journal of Computational Mathematics·Volume 03 (2013)·Pages 37–42·Published 26 March 2013·DOI10.4236/ajcm.2013.31005
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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 al gorithm and mixed Gaussian distribution probability. This article explores the segment-mean algorithm for dimension ality 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.
KeywordsEnglish Sentence RecognitionHMMClustering
- M. Zhu, X. Wen, J. Huang and L. Zhou, “Computer Speech Technology,” Revised Edition, Beijing University of Aeronautics and Astronautics Press, Beijing, 2002.
- L. R. Rabiner, “A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition,” Proceeding of the IEEE, Vol. 77, No. 2, 1989, pp. 257-286. doi:10.1109/5.18626
- L. R. Rabiner and B. H. Juang, “Fundamentals of Speech Recognition,” 1st Edition, Prentice Hall, Upper Saddle River, 1993.
- Q. He and Y. He, “An Extension of MATLAB Programming,” 1st Edition, Tsinghua University Press, Beijing, 2002.
- J. Han, L. Zhang and T. Zheng, “Speech Signal Processing,” 1st Edition, Tsinghua University Press, Beijing, 2004.
- L. Lippmann, E. Martin and D. Paul, “Multi-Style Training for Robust Isolated-Word Speech Recognition,” Proceedings of the 1987 IEEE International Conference on Acoustics, Speech and Signal Processing, Dallas, 6-9 April 1987, pp. 705-708.
- L. R. Rabiner, J. G. Wilpon and F. K. Soong, “High Performance Connected Digit Recognition Using Hidden Markov Model,” Proceedings of the 1988 IEEE International Conference on Acoustics, Speech and Signal Processing, New York, 11-14 April 1988, pp. 119-122.
- Y. Bao, J. Zheng and X. Wu, “Speech Recognition Based on a Hybrid Model of Hidden Markov Models and the Genetic Algorithm Neural Network,” Computer Engineering & Science, Vol. 33, No. 4, 2011, pp. 139-144.
- S. K. Bhatia, “Adaptive K-Means Clustering,” Proceedings of the Seventeenth International Florida Artificial Intelligence Research Society Conference, Miami, 12-14 May 2004, pp. 695-699.
- A. Likas, N. Vlassis and J. Verbeek, “The Global K-Means Clustering Algorithm,” Pattern Recognition, Vol. 36, No. 2, 2003, pp. 451-461. doi:10.1016/S0031-3203(02)00060-2
- T. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman and A. Y. Wu, “An Efficient K-Means Clustering Algorithms Analysis and Implementation,” IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 24, No. 7, 2002, pp. 881-892. doi:10.1109/TPAMI.2002.1017616
- X. Ma, Y. Fu and J. Lu, “The Segmental Fuzzy c-Means Algorithm for Estimating Parameters of Continuous Density Hidden Markov Models,” Acta Acustica, Vol. 22, No. 6, 1997, pp. 550-554.
- L. Zhao, C. Zou and Z. Wu, “The Segmental Fuzzy Clustering Algorithm for Estimating Parameters of the VQ-HMM,” Journal of Circuits and Systems, Vol. 7, No. 3, 2002, pp. 66-69.