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| Title: | Kernel eigenspace-based MLLR adaptation using multiple regression classes |
| Authors: | Hsiao, Roger
Mak, Brian Kan-Wing |
| Keywords: | Kernel methods
Eigenvoice-based adaptation methods
Kernel eigenvoice adaptation
Multiple regression classes |
| Issue Date: | Mar-2005 |
| Citation: | IEEE International Conference on Acoustics, Speech, and Signal Processing, March 18-23, 2005, Philadelphia, USA, vol. 1, p. 985-988 |
| Abstract: | Recently, we have been investigating the application of kernel methods to improve the performance of eigenvoice-based adaptation methods by exploiting possible nonlinearity in their original working space. We proposed the kernel eigenvoice adaptation (KEV) in [1], and the kernel eigenspace-based MLLR adaptation (KEMLLR) in [2]. In KEMLLR, speaker-dependent MLLR transformation matrices are mapped to a kernel-induced high dimensional feature space, and kernel principal component analysis (KPCA) is used to derive a set of eigenmatrices in the feature space. A new speaker is then represented by a linear combination of the leading eigenmatrices. In this paper, we furthur improve KEMLLR by the use of multiple regression classes and the quasi-Newton BFGS optimization algorithm. |
| Rights: | © 2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. |
| URI: | http://hdl.handle.net/1783.1/2303 |
| Appears in Collections: | CSE Conference Papers
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| icassp2005kemllr.pdf | pre-published version | 113Kb | Adobe PDF | View/Open |
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