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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.

has been cited by the following article:

• TITLE: English Sentence Recognition Based on HMM and Clustering

  AUTHORS: Xinguang Li, Jiahua Chen, Zhenjiang Li

  KEYWORDS: English Sentence Recognition; HMM; Clustering

  JOURNAL NAME: American Journal of Computational Mathematics, Vol.3 No.1, March 27, 2013

  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.