The performance and robustness of a speech recognition system can be improved by
adapting the speech models to the speaker, the channel and the task. In continuous mixture …

Adapting the parameters of a statistical speaker independent continuous-speech recognizer
to the speaker and the channel can significantly improve the recognition performance and …

A trend in automatic speech recognition systems is the use of continuous mixture-density
hidden Markov models (HMMs). Despite the good recognition performance that these …

We propose an improved maximum a posteriori (MAP) learning algorithm of continuous-
density hidden Markov model (CDHMM) parameters for speaker adaptation. The algorithm …

In previous work, we showed how to constrain the estimation of continuous mixture-density
hidden Markov models (HMMs) when the amount of adaptation data is small. We used …

An alternative approach to speaker adaptation for a large-vocabulary hidden-Markov-model-
based speech recognition system is described. The goal of this investigation was to train the …

A speaker adaptation method for continuous density HMMs, which performs well for any
amount of data for adaptation, is proposed. This method estimates shift parameters for the …

Most adaptation methods for speech recognition using hidden Markov models fall into two
categories; one is the Bayesian approach, where prior distributions for the model …

A number of issues related to the application of Bayesian learning techniques to speaker
adaptation are investigated. It is shown that the seed models required to construct prior …

An instantaneous speaker adaptation method is proposed that uses N-best decoding for
continuous mixture-density hidden Markov model-based speech recognition systems. An N …