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}, |
| Keywords: | |
| Projects: |
Idiap |
| Authors: | |
| Crossref by |
misr03 |
| Added by: | [UNK] |
| Total mark: | 0 |
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