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    Isolated and continuous bangla speech recognition: implementation, performance and application perspective

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    Isolated and Continuous Bangla Speech Recognition.pdf (226.0Kb)
    Date
    2007
    Publisher
    BRAC University
    Author
    Hasnat, Md. Abul
    Mowla, Jabir
    Khan, Mumit
    Metadata
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    URI
    http://hdl.handle.net/10361/331
    Abstract
    Research 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).
    Keywords
    Bangla speech recognition; Bangla language processing
     
    Description
    Includes bibliographical references (page 6).
    Department
    Center for Research on Bangla Language Processing (CRBLP), BRAC University
    Collections
    • Conference Papers (Centre for Research on Bangla Language Processing)

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