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A BASIS METHOD FOR ROBUST ESTIMATION OF CONSTRAINED MLLR

Adaptation for ASR

Full Paper at IEEE Xplore

Presented by: Daniel Povey, Author(s): Daniel Povey, Kaisheng Yao, Microsoft Corporation, United States

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 estimate a smaller or larger number of coefficients.


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  Lecture Information

Recorded: 2011-05-24 17:55 - 18:15, Panorama
Added: 15. 6. 2011 15:13
Number of views: 45
Video resolution: 1024x576 px, 512x288 px
Video length: 0:19:14
Audio track: MP3 [6.50 MB], 0:19:14