Information geometry of topology preserving adaptation

We consider adaptation by topologically smooth transformations with applications to environment and speaker adaptation for robust speech recognition. Specifically, the tradeoff between global affine transformations that fail to capture local variation but preserve topology and local class dependent...

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Published in2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100) Vol. 6; pp. 3743 - 3746 vol.6
Main Author Sonmez, M.K.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2000
Subjects
Information geometry
Kernel
Minimization methods
Parameter estimation
Robustness
Solid modeling
Speech recognition
Stochastic processes
Topology
Vector quantization
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Summary:We consider adaptation by topologically smooth transformations with applications to environment and speaker adaptation for robust speech recognition. Specifically, the tradeoff between global affine transformations that fail to capture local variation but preserve topology and local class dependent bias transformations that have more resolution but may destroy the topology of the reference model is addressed. We cast the problem of topology preservation of the reference model in an information divergence geometry framework and derive a class of alternating minimization algorithms that aims to preserve topology explicitly during adaptation.
ISBN:9780780362932
0780362934
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2000.860216