A Bayesian model selection criterion for HMM topology optimization
Alain Biem, Jin-Young Ha, J. Subrahmonia · IEEE International Conference on Acoustics Speech and Signal Processing · 2002
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 Bayesian selection criteria, such as Schwarz's Bayesian Information Criterion (BIC). Unlike, BIC, which uses a multivariate Normal distribution assumption for the prior of all parameters of the model, the proposed HMM-oriented Bayesian Information Criterion (HBIC), models each parameter by a different distribution, one more appropriate for that parameter The results on an handwriting recognition task shows that the HBIC realizes a much smaller and efficient system than a system generated through the BIC.