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A Voice Trigger System using Keyword and Speaker Recognition for Mobile Devices

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dc.contributor.authorLee, Hyeopwoo-
dc.contributor.authorChang, Sukmoon-
dc.contributor.authorYook, Dongsuk-
dc.contributor.authorKim, Yongserk-
dc.date.accessioned2021-09-08T11:58:05Z-
dc.date.available2021-09-08T11:58:05Z-
dc.date.created2021-06-11-
dc.date.issued2009-11-
dc.identifier.issn0098-3063-
dc.identifier.urihttps://scholar.korea.ac.kr/handle/2021.sw.korea/118992-
dc.description.abstractVoice activity detection plays an important role for an efficient voice interface between human and mobile devices, since it can be used as a trigger to activate an automatic speech recognition module of a mobile device. If the input speech signal can be recognized as a predefined magic word coming from a legitimate user, it can be utilized as a trigger. In this paper, we propose a voice trigger system using a keyword-dependent speaker recognition technique. The voice trigger must be able to perform keyword recognition, as well as speaker recognition, without using computationally demanding speech recognizers to properly trigger a mobile device with low computational power consumption. We propose a template based method and a hidden Markov model (HMM) based method for the voice trigger to solve this problem. The experiments using a Korean word corpus show that the template based method performed 4.1 times faster than the HMM based method However, the HMM based method reduced the recognition error by 27.8% relatively compared to the template based method The proposed methods are complementary and can be used selectively depending on the device of interest.(1)-
dc.languageEnglish-
dc.language.isoen-
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC-
dc.subjectHIDDEN MARKOV-MODELS-
dc.subjectSPEECH RECOGNITION-
dc.subjectVERIFICATION-
dc.subjectIDENTIFICATION-
dc.subjectALGORITHM-
dc.titleA Voice Trigger System using Keyword and Speaker Recognition for Mobile Devices-
dc.typeArticle-
dc.contributor.affiliatedAuthorYook, Dongsuk-
dc.identifier.doi10.1109/TCE.2009.5373813-
dc.identifier.scopusid2-s2.0-75449114041-
dc.identifier.wosid000273177100088-
dc.identifier.bibliographicCitationIEEE TRANSACTIONS ON CONSUMER ELECTRONICS, v.55, no.4, pp.2377 - 2384-
dc.relation.isPartOfIEEE TRANSACTIONS ON CONSUMER ELECTRONICS-
dc.citation.titleIEEE TRANSACTIONS ON CONSUMER ELECTRONICS-
dc.citation.volume55-
dc.citation.number4-
dc.citation.startPage2377-
dc.citation.endPage2384-
dc.type.rimsART-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalResearchAreaTelecommunications-
dc.relation.journalWebOfScienceCategoryEngineering, Electrical & Electronic-
dc.relation.journalWebOfScienceCategoryTelecommunications-
dc.subject.keywordPlusHIDDEN MARKOV-MODELS-
dc.subject.keywordPlusSPEECH RECOGNITION-
dc.subject.keywordPlusVERIFICATION-
dc.subject.keywordPlusIDENTIFICATION-
dc.subject.keywordPlusALGORITHM-
dc.subject.keywordAuthorVoice trigger-
dc.subject.keywordAuthorkeyword recognition-
dc.subject.keywordAuthorspeaker recognition-
dc.subject.keywordAuthordynamic time warping-
dc.subject.keywordAuthorvector quantization-
dc.subject.keywordAuthorGaussian mixture model-
dc.subject.keywordAuthorhidden Markov model-
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