A stochastic version of Expectation Maximization algorithm for better estimation of Hidden Markov Model

S. Huda, J. Yearwood, Roberto Togneri

    Research output: Contribution to journalArticle

    16 Citations (Scopus)

    Abstract

    This paper attempts to overcome the local convergence problem of the Expectation Maximization (EM) based training of the Hidden Markov Model (HMM) in speech recognition. We propose a hybrid algorithm, Simulated Annealing Stochastic version of EM (SASEM), combining Simulated Annealing with EM that reformulates the HMM estimation process using a stochastic step between the EM steps and the SA. The stochastic processes of SASEM inside EM can prevent EM from converging to a local maximum and find improved estimation for HMM using the global convergence properties of SA. Experiments on the TIMIT speech corpus show that SASEM obtains higher recognition accuracies than the EM.
    Original languageEnglish
    Pages (from-to)1301-1309
    JournalPattern Recognition Letters
    Volume30
    Issue number14
    DOIs
    Publication statusPublished - 2009

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