%Aigaion2 BibTeX export from Idiap Publications
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@TECHREPORT{morris-RR-01-14,
                      author = {Morris, Andrew and Hagen, Astrid and Bourlard, Herv{\'{e}}},
                    keywords = {missing data, multi-band, multi-band combination, multi-stream, robust ASR},
                    projects = {Idiap},
                       title = {MAP Combination of Multi-Stream HMM or HMM/ANN Experts},
                        type = {Idiap-RR},
                      number = {Idiap-RR-14-2001},
                        year = {2001},
                 institution = {IDIAP},
                    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.},
                         pdf = {https://publications.idiap.ch/attachments/reports/2001/rr01-14.pdf},
                  postscript = {ftp://ftp.idiap.ch/pub/reports/2001/rr01-14.ps.gz},
ipdmembership={speech},
}