Source normalization training for HMM modeling of speech
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United States of America Patent
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Dec 27, 2005
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app pub date -
Jun 7, 2000
filing date -
Aug 15, 1998
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Abstract
A maximum likelihood (ML) linear regression (LR) solution to environment normalization is provided where the environment is modeled as a hidden (non-observable) variable. By application of an expectation maximization algorithm and extension of Baum-Welch forward and backward variables (Steps 23a-23d) a source normalization is achieved such that it is not necessary to label a database in terms of environment such as speaker identity, channel, microphone and noise type.
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