IMPROVEMENT OF THE TEXT DEPENDENT SPEAKER IDENTIFICATION SYSTEM USING DISCRETE MMM WITH CEPSTRAL BASED FEATURES

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dc.contributor.authorIslam, Md. Rabiul
dc.contributor.authorRahman, Md. Fayzur
dc.contributor.authorKhan, 3Muhammad Abdul Goffar
dc.date.accessioned2012-11-10T09:58:40Z
dc.date.accessioned2019-05-29T05:04:04Z
dc.date.available2012-11-10T09:58:40Z
dc.date.available2019-05-29T05:04:04Z
dc.date.issued2011-07-01
dc.description.abstractIn this paper, an improved strategy for automated text based speaker identification scheme has been proposed. The identification process incorporates the Hidden Markov Model technique. After preprocessing the speech, HMM is used in the learning and identification. Features are extracted by different techniques such as RCC, MFCC, ÄMFCC, ÄÄMFCC, LPC and LPCC which is almost different in each case. The highest identification rate of 93% has been achieved in the close set text dependent speaker identification system.
dc.identifier.otherhttp://dspace.daffodilvarsity.edu.bd:8080/handle/20.500.11948/543
dc.identifier.urihttp://hdl.handle.net/20.500.11948/543
dc.language.isoen
dc.publisherDaffodil International University
dc.sourceDIU Institutional Repository
dc.subjectBiometric Technologies, Automatic Speaker Identification, Cepstral Coefficients, Feature Extraction, Hidden Markov Model.
dc.titleIMPROVEMENT OF THE TEXT DEPENDENT SPEAKER IDENTIFICATION SYSTEM USING DISCRETE MMM WITH CEPSTRAL BASED FEATURES
dc.typeArticle

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Vol. 06, Issue 2, July 2011