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Entropy-based Multi-stream Combination
Type of publication: Idiap-RR
Citation: misra-rr-02-31
Number: Idiap-RR-31-2002
Year: 2002
Institution: IDIAP
Address: Martigny, Switzerland
Note: {in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing {(ICASSP)}, 2003}
Abstract: Full-combination multi-band approach has been proposed in the literature and performs well for band-limited noise. But the approach fails to deliver in case of wide-band noise. To overcome this, multi-stream approaches are proposed in literature with varying degree of success. Based on our observation that for a classifier trained on clean speech, the entropy at the output of the classifier increases in presence of noise at its input, we used entropy as a measure of confidence to give weightage to a classifier output. In this paper, we propose a new entropy based combination strategy for full-combination multi-stream approach. In this entropy based approach, a particular stream is weighted inversely proportional to the output entropy of its specific classifier. A few variations of this basic approach are also suggested. It is observed that the word-error-rate (WER) achieved by the proposed combination methods is better for different types of noises and for their different signal-to-noise-ratios (SNRs). Some interesting relationship is observed between the WER performances of different combination methods and their respective entropies.
Userfields: ipdinar={2002}, ipdmembership={speech}, language={English},
Projects Idiap
Authors Misra, Hemant
Bourlard, Hervé
Tyagi, Vivek
Crossref by misr03
Added by: [UNK]
Total mark: 0
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