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A Variable Initialization Approach to the EM Algorithm for Better Estimation of the Parameters of Hidden Markov Model Based Acoustic Modeling of Speech Signals

  • Md. Shamsul Huda
  • Ranadhir Ghosh
  • John Yearwood
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4065)

Abstract

The traditional method for estimation of the parameters of Hidden Markov Model (HMM) based acoustic modeling of speech uses the Expectation-Maximization (EM) algorithm. The EM algorithm is sensitive to initial values of HMM parameters and is likely to terminate at a local maximum of likelihood function resulting in non-optimized estimation for HMM and lower recognition accuracy. In this paper, to obtain better estimation for HMM and higher recognition accuracy, several candidate HMMs are created by applying EM on multiple initial models. The best HMM is chosen from the candidate HMMs which has highest value for likelihood function. Initial models are created by varying maximum frame number in the segmentation step of HMM initialization process. A binary search is applied while creating the initial models. The proposed method has been tested on TIMIT database. Experimental results show that our approach obtains improved values for likelihood function and improved recognition accuracy.

Keywords

Hide Markov Model Speech Signal Gaussian Mixture Model Recognition Accuracy Binary Search 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Md. Shamsul Huda
    • 1
  • Ranadhir Ghosh
    • 1
  • John Yearwood
    • 1
  1. 1.School of Information Technology and Mathematical ScienceUniversity of BallaratBallaratAustralia

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