Maximum likelihood based discriminative training of acoustic models
Document typeConference report
PublisherEuropean Speech Communication Association (ESCA)
Rights accessOpen Access
In this paper, a framework for discriminative training of acoustic models based on Generalised Probabilistic Descent (GPD) method is presented. The key feature of our proposal, Maximum Likelihood based Discriminative Training of Acoustic Models (MLDT), is the use of maximum likelihood trained HMM's instead of the original speech signal. We focus our attention in performing discriminative training applied to a discrete hidden Markov models continuos speech recogniser, achieving a 4.6% error rate reduction on a Spanish speaker-independent phoneme recognition task.
CitationNogueiras, A., Mariño, J.B. Maximum likelihood based discriminative training of acoustic models. A: European Conference on Speech Communication and Technology (EUROSPEECH). "EUROSPEECH '95: 4th European Conference on Speech Communication and Technology: Madrid, Spain: 18-21 September 1995". Madrid: European Speech Communication Association (ESCA), 1995, p. 85-88.