Evaluating the effect of voice activity detection in isolated Yoruba word recognition system

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dc.contributor.authorAibinu, Abiodun M.
dc.contributor.authorSalami, Momoh-Jimoh E.
dc.contributor.authorNajeeb, Athaur R.
dc.contributor.authorAzeez, J. F.
dc.contributor.authorRajin, Ataul K.
dc.date.accessioned2019-10-24T13:49:23Z
dc.date.available2019-10-24T13:49:23Z
dc.date.issued2011-05-17
dc.description.abstractThis paper discusses and evaluates the effect of voice Activity Detection (VAD) in an isolated Yoruba word recognition system (IYWRS). The word database used in this paper are collected from 22 speakers by repeating the numbers 1 to 9 three times each. A hybrid configuration of Mel-Frequency Cepstral coefficient (MFCC) and Linear Predictive Coding (LPC) have been used to extract the features of the speech samples. Artificial Neural Network algorithms are then used to classify these features. An overall accuracy of about 60% has been achieved from the two proposed feature extraction methods.en_US
dc.identifier.citationAibinu, A. M., Salami, M. E., Najeeb, A. R., Azeez, J. F., & Rajin, S. A. K. (2011, May). Evaluating the effect of voice activity detection in isolated Yoruba word recognition system. In 2011 4th International Conference on Mechatronics (ICOM) (pp. 1-5). IEEE.en_US
dc.identifier.uri10.1109/ICOM.2011.5937134
dc.identifier.urihttp://repository.elizadeuniversity.edu.ng/handle/20.500.12398/614
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectSpeech recognitionen_US
dc.subjectFeature extractionen_US
dc.subjectSpeechen_US
dc.subjectMel frequency cepstral coefficienten_US
dc.subjectArtificial neural networksen_US
dc.subjectAccuracyen_US
dc.subjectTrainingen_US
dc.titleEvaluating the effect of voice activity detection in isolated Yoruba word recognition systemen_US
dc.typeArticleen_US
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