ISCA Archive Eurospeech 1999
ISCA Archive Eurospeech 1999

Incremental training of CDHMMs using bayesian learning

Claudio Vair, Massimiliano Mercogliano, Luciano Fissore

The Bayesian Learning approach (MAP Maximum A Posteriori) can be used for the incremental training of Continuous Density Hidden Markov Models (CDHMM), performed through speech data collected in real applications. The effectiveness of MAP is heavily conditioned by the correct balance between the apriori knowledge and the field training data. In this paper we propose and evaluate several optimization methods of the MAP combination function, based either on maximum likelihood (ML) and heuristics criteria. To adjust the relevance of the apriori knowledge we use the exponential forgetting technique into the MAP framework. We present several tests that compare the error rate reduction as a function of the selected optimization method and of the size of adaptation data.


doi: 10.21437/Eurospeech.1999-606

Cite as: Vair, C., Mercogliano, M., Fissore, L. (1999) Incremental training of CDHMMs using bayesian learning. Proc. 6th European Conference on Speech Communication and Technology (Eurospeech 1999), 2753-2756, doi: 10.21437/Eurospeech.1999-606

@inproceedings{vair99_eurospeech,
  author={Claudio Vair and Massimiliano Mercogliano and Luciano Fissore},
  title={{Incremental training of CDHMMs using bayesian learning}},
  year=1999,
  booktitle={Proc. 6th European Conference on Speech Communication and Technology (Eurospeech 1999)},
  pages={2753--2756},
  doi={10.21437/Eurospeech.1999-606}
}