On-line adaptation of hidden Markov models using incremental estimation algorithms

The mismatch that frequently occurs between the training and testing conditions of an automatic speech recognizer can be e ciently 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.