A CONSTRAINED LINE SEARCH APPROACH TO GENERAL DISCRIMINATIVE HMM TRAINING

摘要

Recently, we proposed a novel optimization algorithm called constrained line search (CLS) to train Gaussian mean vectors of HMMs in the MMI sense. In this paper, we extend and re-formulate it in a more general framework. The new CLS can optimize any discriminative objective functions including MMI, MCE, MPE/MWE etc. Also, closed-form solutions to update all Gaussian mixture parameters, including means, covariances and mixture weights, are obtained. We investigate the new CLS on several benchmark speech recognition databases, including TIDIGITS, Switchboard mini-train and Switchboard full h5train00 sets. Experimental results show that the new CLS optimization method outperforms the conventional EB W method in both performance and convergence behavior.