Deleted strategy for MMI-based HMM training

We apply the maximum mutual information (MMI) criterion to discriminative training of hidden Markov model (HMM) parameters. In contrast to the conventional MMI training approach, we adopt the cross-validatory strategy with which the parameters are estimated on a part and assessed on the other parts of the training data. For this purpose, we propose the deleted MMI training method, which performs cross-validatory parameter updating while maintaining the converging behavior of the conventional MMI-based algorithm. The proposed method is compared to the conventional MMI approach in classification of artificial data and in speaker-independent continuous speech recognition, and shows better performance.