A combined maximum mutual information and maximum likelihood approach for mixture density splitting

In this work a method for splitting continuous mixture density hidden Markov models (HMM) is presented. The approach combines a model evaluation measure based on the Maximum Mutual Information (MMI) criterion with subsequent standard Maximum Likelihood (ML) training of the HMM parameters. Experiments were performed on the SieTill corpus for telephone line recorded German continuous digit strings. The proposed splitting approach performed better than discriminative training with conventional splitting and as good as discriminative training after the new splitting approach.