Dynamic threshold setting via Bayesian information criterion (BIC) in HMM training

In this paper, an approach of dynamic threshold setting via Bayesian Information Criterion (BIC) in HMM training is described. The BIC threshold setting is applied to two important applications. Firstly, it is used to set the thresholds for decision tree based state tying, in place of the conventional approach of using a heuristic constant threshold. Secondly, it is applied to choosing the number of Gaussian mixture at state mixing-up stage. Experimental results on LVCSR Chinese dictation task indicate that BIC can dynamically set thresholds for cluster splitting according to the underlying complexity of the cluster parameters. Also significant performance improvement is achieved with the dynamic BIC threshold setting.