MAP Combination of Multi-Stream HMM or HMM/ANN Experts
| Type of publication: | Conference paper |
| Citation: | eurospeech01 |
| Booktitle: | Proc. Eurospeech |
| Year: | 2001 |
| Address: | Aalborg, Denmark |
| Crossref: | morris-rr-01-14: |
| Abstract: | Automatic speech recognition (ASR) performance falls dramatically with the level of mismatch between training and test data. The human ability to recognise speech when a large proportion of frequencies are dominated by noise has inspired the "missing data" and "multi-band" approaches to noise robust ASR. "Missing data" ASR identifies low SNR spectral data in each data frame and then ignores it. Multi-band ASR trains a separate model for each position of missing data, estimates a reliability weight for each model, then combines model outputs in a weighted sum. A problem with both approaches is that local data reliability estimation is inherently inaccurate and also assumes that all of the training data was clean. In this article we present a model in which adaptive multi-band expert weighting is incorporated naturally into the maximum a posteriori (MAP) decoding process. |
| Userfields: | ipdmembership={speech}, |
| Keywords: | missing data, multi-band, multi-band combination, multi-stream, robust ASR |
| Projects |
Idiap |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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