Covariance matrix estimation with structural-based priors for speech processing
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United States of America Patent
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Aug 21, 2018
Grant Date -
Mar 9, 2017
app pub date -
Sep 6, 2015
filing date -
Sep 6, 2015
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Abstract
According to some embodiments of the present invention there is provided a computerized method for speech processing using a Gaussian Mixture Model. The method comprises the action of receiving by hardware processor(s) two or more covariance values representing relationships between distributions of speech coefficient values that represent two or more audible input speech signals recorded by a microphone. The method comprises the action of computing two or more eigenvectors and eignevalues using a principle component analysis of the covariance values and transforming the speech coefficient values using the eigenvectors and computing two or more second covariance values from the transformed speech coefficient values. The method comprises the action of modifying some of the second covariance values according to the eignevalues, the covariance values, and two or more indices of the speech coefficient values. The second covariance values to the speech processor comprising the Gaussian Mixture Model.
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