Constrained Maximum Likelihood Linear Regression (CMLLR) is a

widely used speaker adaptation technique in which an affine transform

of the features is estimated for each speaker. However, when

the amount of speech data available is very small (e.g. a few seconds),

it can be difficult to get sufficiently accurate estimates of the

transform parameters. In this paper we describe a method of estimating

CMLLR robustly from less data. We do this by representing the

CMLLR transform matrix as a weighted sum over basis matrices,

where the basis is constructed in such a way that the most important

variation is concentrated in the leading coefficients. Depending on

the amount of data available, we can choose to estimate a smaller or

larger number of coefficients.

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In  ICASSP

Publisher  IEEE

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