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

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dc.contributor.authorSuh, Youngjooko
dc.contributor.authorKim, Hoirinko
dc.date.accessioned2016-04-20T06:08:50Z-
dc.date.available2016-04-20T06:08:50Z-
dc.date.created2015-12-01-
dc.date.created2015-12-01-
dc.date.created2015-12-01-
dc.date.issued2015-12-
dc.identifier.citationIEEE SIGNAL PROCESSING LETTERS, v.22, no.12, pp.2421 - 2424-
dc.identifier.issn1070-9908-
dc.identifier.urihttp://hdl.handle.net/10203/205115-
dc.description.abstractIn 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.-
dc.languageEnglish-
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC-
dc.titleProbabilistic Class Histogram Equalization Based on Posterior Mean Estimation for Robust Speech Recognition-
dc.typeArticle-
dc.identifier.wosid000364207300001-
dc.identifier.scopusid2-s2.0-84960337143-
dc.type.rimsART-
dc.citation.volume22-
dc.citation.issue12-
dc.citation.beginningpage2421-
dc.citation.endingpage2424-
dc.citation.publicationnameIEEE SIGNAL PROCESSING LETTERS-
dc.identifier.doi10.1109/LSP.2015.2490202-
dc.contributor.localauthorKim, Hoirin-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorCDF estimation-
dc.subject.keywordAuthorfeature normalization-
dc.subject.keywordAuthorhistogram equalization-
dc.subject.keywordAuthorposterior mean-
dc.subject.keywordAuthorrobust speech recognition-
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