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Abstract
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Article Information:
Parameter Compensation for Mel-LP based Noisy Speech Recognition
Md. Mahfuzur Rahman, Md. Robiul Hoque and M. Babul Islam
Corresponding Author: M. Babul Islam
Submitted: 2011 July, 13
Accepted: 2011 August, 30
Published: 2012 March, 10 |
Abstract:
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This study deals with a noise robust distributed speech recognizer for real-world applications by
deploying feature parameter compensation technique. To realize this objective, Mel-LP based speech analysis
has been used in speech coding on the linear frequency scale by applying a first-order all-pass filter instead of
a unit delay. To minimize the mismatch between training and test phases, Cepstral Mean Normalization (CMN)
and Blind Equalization (BEQ) have been applied to enhance Mel-LP cepstral coefficients as an effort to reduce
the effect of additive noise and channel distortion. The performance of the proposed system has been evaluated
on Aurora-2 database which is a subset of TIDigits database contaminated by additive noises and channel
effects. The baseline performance, that is, for Mel-LPC the average word accuracy for test set A has found to
be 59.05%. By applying the CMN and BEQ with the Mel-LP cepstral coefficients, the performance has been
improved to 68.02 and 65.65%, respectively.
Key words: Aurora-2 database, BEQ, bilinear transformation, CMN, Mel-LPC, ,
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Cite this Reference:
Md. Mahfuzur Rahman, Md. Robiul Hoque and M. Babul Islam, . Parameter Compensation for Mel-LP based Noisy Speech Recognition. Research Journal of Information Technology , (1): 7-12.
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ISSN (Online): 2041-3114
ISSN (Print): 2041-3106 |
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