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dc.contributor.authorSon, Guiyoung-
dc.contributor.authorKwon, Soonil-
dc.contributor.authorLim, Yoonseob-
dc.date.accessioned2024-01-20T02:32:37Z-
dc.date.available2024-01-20T02:32:37Z-
dc.date.created2021-09-05-
dc.date.issued2017-01-
dc.identifier.issn1582-7445-
dc.identifier.urihttps://pubs.kist.re.kr/handle/201004/123260-
dc.description.abstractAlthough smart devices have become a widely-adopted tool for communication in modern society, it still requires a steep learning curve among the elderly. By introducing a voice-based interface for smart devices using voice recognition technology, smart devices can become more user-friendly and useful to the elderly. However, the voice recognition technology used in current devices is attuned to the voice patterns of the young. Therefore, speech recognition falters when an elderly user speaks into the device. This paper has identified that the elderly's improper speech rate by each syllable contributes to the failure in the voice recognition system. Thus, upon modifying the speech rate by each syllable, the voice recognition rate saw an increase of 12.3%. This paper demonstrates that by simply modifying the speech rate by each syllable, which is one of the factors that causes errors in voice recognition, the recognition rate can be substantially increased. Such improvements in voice recognition technology can make it easier for the elderly to operate smart devices that will allow them to be more socially connected in a mobile world and access information at their fingertips. It may also be helpful in bridging the communication divide between generations.-
dc.languageEnglish-
dc.publisherUNIV SUCEAVA, FAC ELECTRICAL ENG-
dc.titleSpeech Rate Control for Improving Elderly Speech Recognition of Smart Devices-
dc.typeArticle-
dc.identifier.doi10.4316/AECE.2017.02011-
dc.description.journalClass1-
dc.identifier.bibliographicCitationADVANCES IN ELECTRICAL AND COMPUTER ENGINEERING, v.17, no.2, pp.79 - 84-
dc.citation.titleADVANCES IN ELECTRICAL AND COMPUTER ENGINEERING-
dc.citation.volume17-
dc.citation.number2-
dc.citation.startPage79-
dc.citation.endPage84-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.identifier.wosid000405378100011-
dc.identifier.scopusid2-s2.0-85020117598-
dc.relation.journalWebOfScienceCategoryComputer Science, Artificial Intelligence-
dc.relation.journalWebOfScienceCategoryEngineering, Electrical & Electronic-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalResearchAreaEngineering-
dc.type.docTypeArticle-
dc.subject.keywordAuthorautomatic speech recognition-
dc.subject.keywordAuthorhuman computer interaction-
dc.subject.keywordAuthorspeech analysis-
dc.subject.keywordAuthorman machine systems-
dc.subject.keywordAuthorhuman factor-
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KIST Article > 2017
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