A Bayesian model selection criterion for HMM topology optimization

  title={A Bayesian model selection criterion for HMM topology optimization},
  author={Alain Biem and Jin-Young Ha and Jayashree Subrahmonia},
  journal={2002 IEEE International Conference on Acoustics, Speech, and Signal Processing},
This paper addresses the problem of estimating the optimal Hidden Markov Model (HMM) topology. The optimal topology is defined as the one that gives the smallest error-rate with the minimal number of parameters. The paper introduces a Bayesian model selection criterion that is suitable for Continuous Hidden Markov Models topology optimization. The criterion is derived from the Laplacian approximation of the posterior of a model structure, and shares the algorithmic simplicity of conventional… CONTINUE READING
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