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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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Strategies for high accuracy keyword detection in noisy channels., , , , , , , , , and 1 other author(s). INTERSPEECH, page 15-19. ISCA, (2013)The 2012 SESAME Multimedia Event Detection (MED) System., , , , , , , , , and 14 other author(s). TRECVID, National Institute of Standards and Technology (NIST), (2012)The VOiCES from a Distance Challenge 2019 Evaluation Plan., , , , , and . CoRR, (2019)Improving robustness against reverberation for automatic speech recognition., , , , , , and . ASRU, page 525-532. IEEE, (2015)A novel approach to soft-mask estimation and Log-Spectral enhancement for robust speech recognition., and . ICASSP, page 4105-4108. IEEE, (2012)Calibration and multiple system fusion for spoken term detection using linear logistic regression., , , , , , and . ICASSP, page 7138-7142. IEEE, (2014)Speech recognition in unseen and noisy channel conditions., , , , , and . ICASSP, page 5215-5219. IEEE, (2017)The 2014 SESAME Multimedia Event Detection and Recounting System., , , , , , , , , and 9 other author(s). TRECVID, National Institute of Standards and Technology (NIST), (2014)Analysis of Complementary Information Sources in the Speaker Embeddings Framework., , , , and . INTERSPEECH, page 3568-3572. ISCA, (2018)Robust Speaker Recognition from Distant Speech under Real Reverberant Environments Using Speaker Embeddings., , , , , , and . INTERSPEECH, page 1106-1110. ISCA, (2018)