Deleted strategy for MMI-based HMM training
Nam Soo Kim, Chong Kwan Un · IEEE Transactions on Speech and Audio Processing · 1998
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.