Maximum a posteriori adaptation for large scale HMM recognizers

We present a framework for maximum a posteriori (MAP) adaptation of large scale HMM recognizers. First we review the standard MAP adaptation for Gaussian mixtures. We then show how MAP can be used to estimated transformations which are shared across many parameters. Finally, we combine both techniques: each of the HMM models is adapted based on an interpolation of MAP estimates obtained under varying degrees of sharing. We evaluate this algorithm for adaptation of a continuous density HMM with 96 K Gaussians and show that very satisfactory improvements can be achieved, especially for adaptation of non-native speakers of American English