Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11889/2455
DC FieldValueLanguage
dc.contributor.authorHanani, Abualsoud
dc.contributor.authorSrouji, Fathi
dc.date.accessioned2016-10-13T05:28:16Z
dc.date.available2016-10-13T05:28:16Z
dc.date.issued2010
dc.identifier.citationen_US
dc.identifier.urihttp://hdl.handle.net/20.500.11889/2455
dc.descriptionSrouji,Fathi:en_US
dc.description.abstractOne successful approach to language recognition is to focus on the most discriminative high level features of languages, such as phones and words. In this paper, we applied a similar approach to acoustic features using a single GMM-tokenizer followed by discriminatively trained language models. A feature selection technique based on the Support Vector Machine (SVM) is used to model higher order n-grams. Three different ways to build this tokenizer are explored and compared using discriminative uni-gram and generative GMM-UBM. A discriminative uni-gram using very large GMM tokenizer with 24,576 components yields an EER of 1.66%, rising to 0.71% when fused with other acoustic approaches, on the NIST‟03 LRE 30s evaluation
dc.language.isoaren_US
dc.publisherمعهد ماسen_US
dc.subject.lcshTranslators (Computer programs)
dc.subject.lcshComputational linguistics
dc.subject.lcshMachine learning
dc.subject.lcshGaussian processes
dc.subject.lcshRadar meteorology
dc.subject.lcshWeather forecasting
dc.titleImproved Language Recognition using Mixture Components Statisticsen_US
dc.title.alternativeen_US
dc.typeStudiesen_US
newfileds.departmenten_US
newfileds.custom-issue-dateen_US
newfileds.corporate-authoren_US
newfileds.conferenceen_US
newfileds.item-access-typeopen_accessen_US
newfileds.thesis-progen_US
newfileds.general-subjectEconomicsen_US
item.grantfulltextopen-
item.languageiso639-1other-
item.fulltextWith Fulltext-
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