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Robust Features in Deep-Learning-Based Speech Recognition.

, , , , , , , , , and . New Era for Robust Speech Recognition, Exploiting Deep Learning, Springer, (2017)

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Non causal deep learning based dereverberation., , and . CoRR, (2020)Informedia @TRECVID 2012., , , , , , , , , and 20 other author(s). TRECVID, National Institute of Standards and Technology (NIST), (2012)Hearing Is Believing: Biologically Inspired Methods for Robust Automatic Speech Recognition., and . IEEE Signal Process. Mag., 29 (6): 34-43 (2012)A Bayesian classifier for spectrographic mask estimation for missing feature speech recognition., , and . Speech Commun., 43 (4): 379-393 (2004)Subband Likelihood-Maximizing Beamforming for Speech Recognition in Reverberant Environments., and . IEEE Trans. Speech Audio Process., 14 (6): 2109-2121 (2006)Robust Features in Deep-Learning-Based Speech Recognition., , , , , , , , , and . New Era for Robust Speech Recognition, Exploiting Deep Learning, Springer, (2017)N-Best List Rescoring Using Syntactic Trigrams., , and . MICAI, volume 2972 of Lecture Notes in Computer Science, page 79-88. Springer, (2004)Parameter sharing in subband likelihood-maximizing beamforming for speech recognition using microphone arrays., and . ICASSP (1), page 881-884. IEEE, (2004)Dynamic speaker adaptation for isolated letter recognition using MAP estimation., and . ICASSP, page 734-737. IEEE, (1983)Feature-based speaker-independent recognition of isolated english letters., , , , , and . ICASSP, page 731-733. IEEE, (1983)