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ALGONQUIN - Learning Dynamic Noise Models From Noisy Speech for Robust Speech Recognition
2001
Neural Information Processing Systems
A challenging, unsolved problem in the speech recognition community is recognizing speech signals that are corrupted by loud, highly nonstationary noise. One approach to noisy speech recognition is to automatically remove the noise from the cepstrum sequence before feeding it in to a clean speech recognizer. In previous work published in Eurospeech, we showed how a probability model trained on clean speech and a separate probability model trained on noise could be combined for the purpose of
dblp:conf/nips/FreyKDA01
fatcat:bxpbknd3ufgtzpxp5k2whnkumm