We present and describe two new speaker adaptation methods which apply principal component analysis to maximum likelihood linear regression (MLLR). If we apply MLLR after transforming the baseline mean vectors by their eigenvectors, the contributions of these eigenvalues to the variance of the estimates for the MLLR matrix are inversely proportional to their corresponding eigenvalues. In the fi...

This paper presents lattice-based maximum likelihood linear regression (MLLR) for unsupervised adaptation. Lattice MLLR accumulates the statistics used in the MLLR transform estimation procedure using a forward-backward pass through a word-lattice of alternative hypotheses rather than assuming that the 1-best transcription is accurate as in standard unsupervised MLLR. This results in the abilit...

We present a new method for unsupervised, fast speaker adaptation that combines the Eigen-MLLR transform approach with discriminative MLLR. We thereby aim to profit both from the performance improvements that are generally provided by a discriminative approach, and from the reliability that Eigen-MLLR has demonstrated in fast adaptation scenarios. We present first evaluation results on the Spok...

MLLR is a parameter transformation technique for both speaker and environment adaptation. When the amount of adaptation data is scarce, it is necessary to do adaptation with regression classes. In this paper, we present a rapid MLLR adaptation algorithm, which is called Multi-layer structure MLLR adaptation with subspace regression classes and tying (SRCMLR). The method groups the Gaussians on ...

We propose a parameter shrinkage adaptation framework to estimate models with only a limited set of adaptation data to improve accuracy for automatic speech recognition, by regularizing an objective function with a sum of parameterwise power q constraint. For the first attempt, we formulate ridge maximum likelihood linear regression (MLLR) and ridge constraint MLLR (CMLLR) with an element-wise ...

Maximum likelihood linear regression (MLLR) is a parameter transformation technique for both speaker and environment adaptation. In this paper the iterative use of MLLR is investigated in the context of large vocabulary speaker independent transcription of both noise free and noisy data. It is shown that iterative application of MLLR can be beneficial especially in situations of severe mismatch...

A new adaptation method called inter-class MLLR has recently been introduced. Inter-class MLLR utilizes relationships among different transformation functions to achieve more reliable estimates of MLLR parameters across multiple classes, and it produces lower word error rates (WER) than conventional MLLR in circumstances where very little speaker-specific adaptation data are available. This pap...

A variety of methods are used for speaker adaptation in speech recognition. In some techniques, such as MAP estimation, only the models with available training data are updated. Hence, large amounts of training data are required in order to have significant recognition improvements. In some others, such as MLLR, where several general transformations are applied to model clusters, the results ar...