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. Two measures that characterize the performance of an adaptation algorithm are the speed with which it adapts to the new conditions, and its computational complexity, which is important for online applications. A family of adaptation algorithms for continuous-density hidden Markov model (HMM) based speech recognizers have appeared that are based on constrained reestimation of the distribution parameters. These algorithms 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 online applications, since multiple passes through the adaptation data are required. 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, reduce the computational requirements, and make them suitable for online applications.
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