Robust parameters for automatic segmentation of speech

Automatic segmentation of speech is an important problem that is useful in speech recognition, synthesis and coding. We explore in this paper, the robust parameter set, weighting function and distance measure for reliable segmentation of noisy speech. It is found that the MFCC parameters, successful...

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Bibliographic Details
Main Authors: SaiJayram, A. K. V., Ramasubramanian, V., Sreenivas, T. V.
Format: Conference Proceeding
Language:English
Subjects:
Acoustic distortion
Computational modeling
Distortion measurement
Estimation
Nonvolatile memory
Robustness
Signal to noise ratio
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Summary:Automatic segmentation of speech is an important problem that is useful in speech recognition, synthesis and coding. We explore in this paper, the robust parameter set, weighting function and distance measure for reliable segmentation of noisy speech. It is found that the MFCC parameters, successful in speech recognition. holds the best promise for robust segmentation also. We also explored a variety of symmetric and asymmetric weighting lifters. from which it is found that a symmetric lifter of the form 1 + A sin 1/2 (πn/L), 0 ≤ n ≤ L − 1, for MFCC dimension L, is most effective. With regard to distance measure, the direct L 2 norm is found adequate.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2002.5743767