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Content Provider | IEEE Xplore Digital Library |
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Author | Pitz, M. Ney, H. |
Copyright Year | 1993 |
Abstract | Vocal tract normalization (VTN) is a widely used speaker normalization technique which reduces the effect of different lengths of the human vocal tract and results in an improved recognition accuracy of automatic speech recognition systems. We show that VTN results in a linear transformation in the cepstral domain, which so far have been considered as independent approaches of speaker normalization. We are now able to compute the Jacobian determinant of the transformation matrix, which allows the normalization of the probability distributions used in speaker-normalization for automatic speech recognition. We show that VTN can be viewed as a special case of Maximum Likelihood Linear Regression (MLLR). Consequently, we can explain previous experimental results that improvements obtained by VTN and subsequent MLLR are not additive in some cases. For three typical warping functions the transformation matrix is calculated analytically and we show that the matrices are diagonal dominant and thus can be approximated by quindiagonal matrices. |
Starting Page | 930 |
Ending Page | 944 |
Page Count | 15 |
File Size | 1619966 |
File Format | |
ISSN | 10636676 |
Volume Number | 13 |
Issue Number | 5 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2005-09-01 |
Publisher Place | U.S.A. |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Cepstral analysis Frequency Automatic speech recognition Loudspeakers Speech recognition Human voice Maximum likelihood linear regression Jacobian matrices Distributed computing Probability distribution vocal tract (length) normalization Linear transformation speaker adaptive modeling and training speaker adaptive recognition speech recognition |
Content Type | Text |
Resource Type | Article |
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