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Joint acoustic factor learning for robust deep neural network based automatic speech recognition.

, , , , , and . ICASSP, page 5025-5029. IEEE, (2016)

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Entropy-based pruning of hidden units to reduce DNN parameters., and . SLT, page 672-679. IEEE, (2016)The Blizzard Challenge 2013 - Indian Language Task., , , , , , , , , and . Blizzard Challenge, ISCA, (2013)Joint acoustic factor learning for robust deep neural network based automatic speech recognition., , , , , and . ICASSP, page 5025-5029. IEEE, (2016)Adaptation of Deep Neural Network Acoustic Models for Robust Automatic Speech Recognition., , , , , and . New Era for Robust Speech Recognition, Exploiting Deep Learning, Springer, (2017)Bandwidth Embeddings for Mixed-Bandwidth Speech Recognition., , , and . INTERSPEECH, page 3203-3207. ISCA, (2019)Speed improvements to Information Retrieval-based dynamic time warping using hierarchical K-Means clustering., and . ICASSP, page 8515-8519. IEEE, (2013)IIIT-H System for MediaEval 2014 QUESST., , and . MediaEval, volume 1263 of CEUR Workshop Proceedings, CEUR-WS.org, (2014)Use of articulatory bottle-neck features for query-by-example spoken term detection in low resource scenarios., and . ICASSP, page 7128-7132. IEEE, (2014)SWS task: Articulatory phonetic units and sliding DTW., , and . MediaEval, volume 807 of CEUR Workshop Proceedings, CEUR-WS.org, (2011)IIIT-H SWS 2013: Gaussian Posteriorgrams of Bottle-Neck Features for Query-by-Example Spoken Term Detection., and . MediaEval, volume 1043 of CEUR Workshop Proceedings, CEUR-WS.org, (2013)