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Evaluating endpoint detection algorithms for isolated word from Malay parliamentary speech

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dc.citedby8
dc.contributor.authorSeman N.en_US
dc.contributor.authorBakar Z.A.en_US
dc.contributor.authorBakar N.A.en_US
dc.contributor.authorMohamed H.F.en_US
dc.contributor.authorAbdullah N.A.S.en_US
dc.contributor.authorRamakrisnan P.en_US
dc.contributor.authorAhmad S.M.S.en_US
dc.contributor.authorid24825478400en_US
dc.contributor.authorid6507862938en_US
dc.contributor.authorid25824639200en_US
dc.contributor.authorid57225099928en_US
dc.contributor.authorid55433263000en_US
dc.contributor.authorid24825389600en_US
dc.contributor.authorid24721182400en_US
dc.date.accessioned2023-12-28T07:17:55Z
dc.date.available2023-12-28T07:17:55Z
dc.date.issued2010
dc.description.abstractThis paper presents the endpoint detection approaches specifically for an isolated word uses Malay spoken speeches from Malaysian Parliamentary session. Currently, there are 7,995 vocabularies of utterances in the database collection and for the purpose of this study; the vocabulary is limited to ten words which are most frequently spoken selected from ten speakers. Endpoint detection, which aims to distinguish the speech and non-speech segments of digital speech signal, is considered as one of the key preprocessing steps in speech recognition system. Proper estimation of the start and end of the speech (versus silence or background noise) avoids the waste of speech recognition evaluations on preceding or ensuing silence. In this study, the endpoint detection and speech segmentation task is achieved by using the short-time energy (STE) and short-time zero crossing (STZC) measures and combination of both approaches. As a result, the Hidden Markov Model (HMM) recognizer derived the recognition accuracy rate of 91.4% for combination of both algorithms, if compared only 86.3% for STE and 82.1% for STZC rate alone. The experiments show that there are many problems arise where there are still misdetection of word boundaries for the words with weak fricative and nasal sounds. Other obstacles issues such as speaking styles or mood of speaking can also cause the recognition performance. �2010 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo5466898
dc.identifier.doi10.1109/INFRKM.2010.5466898
dc.identifier.epage296
dc.identifier.scopus2-s2.0-77953879739
dc.identifier.spage291
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-77953879739&doi=10.1109%2fINFRKM.2010.5466898&partnerID=40&md5=09742bf78ae947336c6796cdf8114a04
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/29658
dc.pagecount5
dc.sourceScopus
dc.sourcetitleProceedings - 2010 International Conference on Information Retrieval and Knowledge Management: Exploring the Invisible World, CAMP'10
dc.subjectEndpoint detection
dc.subjectInfinite impulse response
dc.subjectMel frequency cepstral coefficient
dc.subjectShort-time energy
dc.subjectShort-time zero crossing
dc.subjectFrequency response
dc.subjectHidden Markov models
dc.subjectImpulse response
dc.subjectInformation retrieval
dc.subjectKnowledge management
dc.subjectEnd point detection
dc.subjectInfinite impulse response
dc.subjectMel-frequency cepstral coefficients
dc.subjectShort-time energy
dc.subjectZero-crossings
dc.subjectSpeech recognition
dc.titleEvaluating endpoint detection algorithms for isolated word from Malay parliamentary speechen_US
dc.typeConference paperen_US
dspace.entity.typePublication
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