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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Dong Kook Kim Young Joon Kim Woo Hyung Lim Nam Soo Kim |
| Copyright Year | 2003 |
| Description | Author affiliation: Sch. of Electr. Eng., Seoul Nat. Univ., South Korea (Dong Kook Kim; Young Joon Kim; Woo Hyung Lim; Nam Soo Kim) |
| Abstract | This paper presents a new approach to online speaker adaptation based on transformation space model evolution. This approach extends the previous idea of speaker space model evolution by applying the a priori knowledge of training speakers to the speaker-dependent maximum likelihood linear regression (MLLR) matrix parameters. A quasi-Bayes (QB) estimation algorithm is devised to incrementally update the hyperparameters of the transformation space model and the regression matrices simultaneously. Experiments on supervised speaker adaptation demonstrate that the proposed approach is more effective compared with the conventional quasi-Bayes linear regression (QBLR) technique when a small amount of adaptation data is available. |
| Sponsorship | IEEE Signal Process, Soc |
| File Size | 302120 |
| File Format | |
| ISBN | 0780376633 |
| ISSN | 15206149 |
| DOI | 10.1109/ICASSP.2003.1198778 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2003-04-06 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Maximum likelihood linear regression Linear regression Principal component analysis Maximum likelihood estimation Parameter estimation Covariance matrix Hidden Markov models Loudspeakers Robustness Data mining |
| Content Type | Text |
| Resource Type | Article |
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