Correlation modeling of MLLR transform biases for rapid HMM adaptation to new speakers

This paper concerns rapid adaptation of hidden Markov model (HMM) based speech recognizers to a new speaker, when only few speech samples (one minute or less) are available from the new speaker. A widely used family of adaptation algorithms defines adaptation as a linearly constrained reestimation of the HMM Gaussians. With few speech data, tight constraints must be introduced, by reducing the number of linear transforms and by specifying certain transform structures (e.g. block diagonal). We hypothesize that under these adaptation conditions, the residual errors of the adapted Gaussian parameters can be represented and corrected by dependency models, as estimated from a training corpus. Thus, after introducing a particular class of linear transforms, we develop correlation models of the transform parameters. In rapid adaptation experiments on the Switchboard corpus, the proposed algorithm performs better than the transform-constrained adaptation and the adaptation by correlation modeling of the HMM parameters, respectively