September 22-25, 1997
The mismatch that frequently occurs between the training and testing conditions of an automatic speech recognizer can be efficiently reduced by adapting the parameters of the recognizer to the testing conditions. The maximum likelihood adaptation algorithms for continuous -density hidden-Markov-model (HMM) based speech recognizers are fast, in the sense that a small amount of data is required for adaptation. They are, however, based on reestimating the model parameters using the batch version of the expectation-maximization (EM) algorithm. The multiple iterations required for the EM algorithm to converge make these adaptation schemes computationally expensive and not suitable for on-line applications, since multiple passes through the adaptation data are required. In this paper we show how incremental versions of the EM and the segmental k- means algorithm can be used to improve the convergence of these adaptation methods so that they can be used in on-line applications.
Bibliographic reference. Digalakis, V. (1997): "On-line adaptation of hidden Markov models using incremental estimation algorithms", In EUROSPEECH-1997, 1859-1862.