Discriminative splitting of Gaussian/log-linear mixture HMMs for speech recognition

This paper presents a method to incorporate mixture density splitting into the acoustic model discriminative log-linear training. The standard method is to obtain a high resolution model by maximum likelihood training and density splitting, and then further training this model discriminatively. For a single Gaussian density per state the log-linear MMI optimization is a global maximum problem, and by further splitting and discriminative training of this model we can get a higher complexity model. The mixture training is not a global maximum problem, nevertheless experimentally we achieve large gains in the objective function and corresponding moderate gains in the word error rate on a large vocabulary corpus.