|Department of Engineering|
|University of Cambridge > Engineering Department > Machine Intelligence Lab|
SPEAKER ADAPTATION OF HMMS USING LINEAR REGRESSION
C.J. Leggetter and P.C. Woodland
A method of speaker adaptation for continuous density HMMs is presented. The model parameters of a general speaker independent system are adapted to a new speaker using a transformation of the mean vectors based on linear regression. The method uses the same maximum likelihood optimisation criteria as Baum-Welch training of model parameters, and can be implemented using the forward-backward algorithm. A full derivation of the transformation is given.
To allow adaptation to be performed on small amounts of data a set of regression classes are defined. The data within each class is pooled to calculate a general regression transformation for that class, and the same transformation is applied to a number of model parameters.
Experiments have been performed on the ARPA RM1 database using a triphone HMM system with mixture Gaussian output distributions. Results show that a 42% reduction in error from the speaker independent system can be achieved by using 40 adaptation utterances from the new speaker.
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