REPORT morris-RR-01-14/IDIAP MAP Combination of Multi-Stream HMM or HMM/ANN Experts Morris, Andrew Hagen, Astrid Bourlard, Hervé missing data multi-band multi-band combination multi-stream robust ASR EXTERNAL https://publications.idiap.ch/attachments/reports/2001/rr01-14.pdf PUBLIC Idiap-RR-14-2001 2001 IDIAP 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.