This paper describes a new rapid speaker adaptation algorithm using a small amount of adaptation data. This algorithm, termed adaptation by correlation (ABC), exploits the intrinsic correlation among speech units to update the speech models. The algorithm updates the means of each Gaussian based on its correlation with means of the Gaussians which are observed in the adaptation data; the updating formula is derived from the theory of least squares. Our experiments on the ARPA NAB-94 evaluation (Eval-94) and the ARPA Hub4-96 (Hub4-96) tasks indicate that ABC seems more stable than MLLR when the amount of data for adaptation is very small (~ 5 seconds), and that ABC seems to enhance MLLR when they are combined.
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