Hybrid simulated annealing and its application to optimization of hidden markov models for visual speech recognition

Jong Seok Lee, Cheol Hoon Park

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

We propose a novel stochastic optimization algorithm, hybrid simulated annealing (SA), to train hidden Markov models (HMMs) for visual speech recognition. In our algorithm, SA is combined with a local optimization operator that substitutes a better solution for the current one to improve the convergence speed and the quality of solutions. We mathematically prove that the sequence of the objective values converges in probability to the global optimum in the algorithm. The algorithm is applied to train HMMs that are used as visual speech recognizers. While the popular training method of HMMs, the expectationmaximization algorithm, achieves only local optima in the parameter space, the proposed method can perform global optimization of the parameters of HMMs and thereby obtain solutions yielding improved recognition performance. The superiority of the proposed algorithm to the conventional ones is demonstrated via isolated word recognition experiments.

Original languageEnglish
Article number5373955
Pages (from-to)1188-1196
Number of pages9
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Volume40
Issue number4
DOIs
Publication statusPublished - 2010 Aug 1

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Hidden Markov models
Simulated annealing
Speech recognition
Global optimization
Mathematical operators
Experiments

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Software
  • Medicine(all)
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this

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