Sixth European Conference on Speech Communication and Technology
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.
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Bibliographic reference. Vair, Claudio / Mercogliano, Massimiliano / Fissore, Luciano (1999): "Incremental training of CDHMMs using bayesian learning", In EUROSPEECH'99, 2753-2756.