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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Eugene Jon Dong Kook Kim Nam Soo Kim |
| Copyright Year | 2001 |
| Description | Author affiliation: Sch. of Electr. & Comput. Eng., Seoul Nat. Univ., South Korea (Eugene Jon) |
| Abstract | We propose a method to enhance the performance of the extended maximum a posteriori (EMAP) estimation using the probabilistic principal component analysis (PPCA). PPCA is used to robustly estimate the correlation matrix among separate hidden Markov model (HMM) parameters. The correlation matrix is then applied to the EMAP scheme for speaker adaptation. PPCA is efficient to compute, and shows better performance compared to the method previously used for EMAP. Through various experiments on continuous digit recognition, it is shown that the EMAP approach based on the PPCA gives enhanced performance especially for a small amount of adaptation data. |
| Sponsorship | IEEE Signal Process. Soc |
| Starting Page | 321 |
| Ending Page | 324 |
| File Size | 341952 |
| Page Count | 4 |
| File Format | |
| ISBN | 0780370414 |
| ISSN | 15206149 |
| DOI | 10.1109/ICASSP.2001.940832 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2001-05-07 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Robustness Principal component analysis Hidden Markov models Speech recognition Training data Maximum likelihood estimation Distribution functions Degradation Adaptation model Probability |
| Content Type | Text |
| Resource Type | Article |
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