{"id":161421,"date":"2011-05-01T00:00:00","date_gmt":"2011-05-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/a-basis-method-for-robust-estimation-of-constrained-mllr\/"},"modified":"2018-10-16T21:52:06","modified_gmt":"2018-10-17T04:52:06","slug":"a-basis-method-for-robust-estimation-of-constrained-mllr","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/a-basis-method-for-robust-estimation-of-constrained-mllr\/","title":{"rendered":"A Basis Method for Robust Estimation of Constrained MLLR"},"content":{"rendered":"<div class=\"asset-content\">\n<p>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.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>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 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