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dc.contributor.authorHasnat, Md. Abul
dc.contributor.authorMowla, Jabir
dc.contributor.authorKhan, Mumit
dc.date.accessioned2010-10-05T05:11:02Z
dc.date.available2010-10-05T05:11:02Z
dc.date.copyright2007
dc.date.issued2007
dc.identifier.urihttp://hdl.handle.net/10361/331
dc.descriptionIncludes bibliographical references (page 6).
dc.description.abstractResearch on automatic speech recognition has been approach progressively since 1930 and the major advances are since 1980 with the introduction of the statistical modeling of speech with the key technology Hidden Markov Model (HMM) and the stochastic language model (B. H. Juang, 2005). However, the existing reported research works on Bangla speech recognition didn’t yet incorporate the HMM technique and language model. This paper presents two different type of Bangla speech recognition from the implementation, performance and application perspective. We used HMM technique for pattern classification and also incorporate stochastic language model with the system. At the signal preprocessing level we perform adaptive noise elimination and end point detection. Spectral feature vectors such as Mel Frequency Cepstral Coefficients(MFCC) with the addition of first and second order coefficients are extracted from each speech wave signal. HMM is used for pattern classification. The system is implemented using the Cambridge Hidden Markov Modeling Toolkit (HTK) (S. Young, 2001-2005).en_US
dc.description.statementofresponsibilityMd. Abul Hasnat
dc.description.statementofresponsibilityJabir Mowla
dc.description.statementofresponsibilityMumit Khan
dc.format.extent6 pages
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.subjectBangla speech recognition
dc.subjectBangla language processing
dc.titleIsolated and continuous bangla speech recognition: implementation, performance and application perspectiveen_US
dc.typeArticleen_US
dc.contributor.departmentCenter for Research on Bangla Language Processing (CRBLP), BRAC University


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