Simultaneous Discriminative Training and Mixture Splitting of HMMs for Speech Recognition

A method is proposed to incorporate mixture density splitting into the acoustic model discriminative training for speech recognition. The standard method is to obtain a high resolution acoustic model by maximum likelihood training and density splitting, and then improving this model by discriminative training. We choose a log-linear form of acoustic model because for a single Gaussian density per triphone state the log-linear MMI optimization is a convex optimization problem, and by further splitting and discriminative training of this model we can get a higher complexity model. Previously it was shown that we achieve large gains in the objective function and corresponding moderate gains in the word error rate on a large vocabulary corpus. This paper incorporates the state of the art minimum phone error training criterion into the framework, and shows that after discriminative splitting, a subsequent log-linear MPE training achieves better results than Gaussian mixture model MPE optimization alone.