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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.
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