CONF astrid-01-05a/IDIAP Adaptive ML-Weighting in Multi-Band Recombination of Gaussian Mixture ASR Hagen, Astrid Bourlard, Hervé Morris, Andrew HMM/ANN-Hybrid ML-adaptation multi-band weighting EXTERNAL https://publications.idiap.ch/attachments/reports/2001/rr01-05.pdf PUBLIC https://publications.idiap.ch/index.php/publications/showcite/astrid-01-05 Related documents ICASSP 1 257-260 2001 Multi-band speech recognition is powerful in band-limited noise, when the recognizer of the noisy band, which is less reliable, can be given less weight in the recombination process. An accurate decision on which bands can be considered as reliable and which bands are less reliable due to corruption by noise is usually hard to take. In this article, we investigate a maximum-likelihood (ML) approach to adapting the combination weights of a multi-band system. The Gaussian Mixture Model parameters are kept constant, while the combination weights are iteratively updated to maximize the data likelihood. Unsupervised offline and online weights adaptation are compared to use of equal weights, and `cheating' weights where the noisy band is known, as well as to the fullband system. Initial tests show that both ML-weighting strategies show a robustness gain on band-limited noise. REPORT astrid-01-05/IDIAP Adaptive ML-Weighting in Multi-Band Recombination of Gaussian Mixture ASR Hagen, Astrid Bourlard, Hervé Morris, Andrew HMM/ANN-Hybrid ML-adaptation multi-band weighting EXTERNAL https://publications.idiap.ch/attachments/reports/2001/rr01-05.pdf PUBLIC Idiap-RR-05-2001 2001 IDIAP Martigny, Switzerland February 2001 Published in: ICASSP, Salt Lake City, Utah, USA, May 2001 Multi-band speech recognition is powerful in band-limited noise, when the recognizer of the noisy band, which is less reliable, can be given less weight in the recombination process. An accurate decision on which bands can be considered as reliable and which bands are less reliable due to corruption by noise is usually hard to take. In this article, we investigate a maximum-likelihood (ML) approach to adapting the combination weights of a multi-band system. The Gaussian Mixture Model parameters are kept constant, while the combination weights are iteratively updated to maximize the data likelihood. Unsupervised offline and online weights adaptation are compared to use of equal weights, and `cheating' weights where the noisy band is known, as well as to the fullband system. Initial tests show that both ML-weighting strategies show a robustness gain on band-limited noise.