In this work a method for splitting continuous mixture density hidden Markov models (HMM) is presented. The approach com-bines a model evaluation measure based on the Maximum Mutual Information (MMI) criterion with subsequent standard Max-imum Likelihood (ML) training of the HMMparameters. Experiments were performed on the SieTill corpus for telephone line recorded German continuous digit strings. The proposed split-ting approach performed better than discriminative training with conventional splitting and as good as discriminative training after the new splitting approach.
Cite as: Schlüter, R., Macherey, W., Müller, B., Ney, H. (1999) A combined maximum mutual information and maximum likelihood approach for mixture density splitting. Proc. 6th European Conference on Speech Communication and Technology (Eurospeech 1999), 1715-1718, doi: 10.21437/Eurospeech.1999-309
@inproceedings{schluter99_eurospeech,
author={Ralf Schlüter and Wolfgang Macherey and Boris Müller and Hermann Ney},
title={{A combined maximum mutual information and maximum likelihood approach for mixture density splitting}},
year=1999,
booktitle={Proc. 6th European Conference on Speech Communication and Technology (Eurospeech 1999)},
pages={1715--1718},
doi={10.21437/Eurospeech.1999-309}
}