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• W2042141988 abstract "We explore time-frequency masking to improve noise robust automatic speech recognition. Apart from its use as a frontend, we use it for providing smooth estimates of speech and noise which are then passed as additional features to a deep neural network (DNN) based acoustic model. Such a system improves performance on the Aurora-4 dataset by 10.5% (relative) compared to the previous best published results. By formulating separation as a supervised mask estimation problem, we develop a unified DNN framework that jointly improves separation and acoustic modeling. Our final system outperforms the previous best system on CHiME-2 corpus by 22.1% (relative)." @default.
• W2042141988 created "2016-06-24" @default.
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• W2042141988 date "2014-05-01" @default.
• W2042141988 modified "2023-09-28" @default.
• W2042141988 title "Joint noise adaptive training for robust automatic speech recognition" @default.
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• W2042141988 doi "https://doi.org/10.1109/icassp.2014.6854051" @default.
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