Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/2303

Kernel Eigenspace-based MLLR adaptation using multiple regression classes

Authors Hsiao, R
Mak, B
Issue Date 2005
Source 2005 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS 1-5: SPEECH PROCESSING , 2005, p. 985-988
Summary 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 further improve KEMLLR by the use of multiple regression classes and the quasi-Newton BFGS optimization algorithm.
Subjects
ISSN 1520-6149
ISBN 0-7803-8874-7
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Language English
Format Conference paper
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