Probabilistic Class Histogram Equalization Based on Posterior Mean Estimation for Robust Speech Recognition

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In this letter, we propose a new probabilistic class histogram equalization technique for noise robust speech recognition. To cope with the sparse data problem which is common in the case of short test data, the proposed histogram equalization technique employs the posterior mean estimator, a kind of the Bayesian estimator, for test CDF. Experiments on the Aurora-4 framework showed that the proposed method produces performance improvement over the conventional maximum likelihood estimation-based approach.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2015-12
Language
English
Article Type
Article
Citation

IEEE SIGNAL PROCESSING LETTERS, v.22, no.12, pp.2421 - 2424

ISSN
1070-9908
DOI
10.1109/LSP.2015.2490202
URI
http://hdl.handle.net/10203/205115
Appears in Collection
EE-Journal Papers(저널논문)
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