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Phoneme-Based Contextualization for Cross-Lingual Speech Recognition in End-to-End Models.

, , , , and . INTERSPEECH, page 2155-2159. ISCA, (2019)

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Application specific loss minimization using gradient boosting., , , and . ICASSP, page 4880-4883. IEEE, (2011)A convex hull approach to sparse representations for exemplar-based speech recognition., , , , and . ASRU, page 59-64. IEEE, (2011)Making Deep Belief Networks effective for large vocabulary continuous speech recognition., , , , , and . ASRU, page 30-35. IEEE, (2011)Optimization Techniques to Improve Training Speed of Deep Neural Networks for Large Speech Tasks., , , and . IEEE Trans. Speech Audio Process., 21 (11): 2267-2276 (2013)Improving Proper Noun Recognition in End-To-End Asr by Customization of the Mwer Loss Criterion., , and . ICASSP, page 7789-7793. IEEE, (2020)Deep Learning for Audio Signal Processing., , , , , and . CoRR, (2019)Semi-supervised Training for End-to-end Models via Weak Distillation., , , and . ICASSP, page 2837-2841. IEEE, (2019)Automatic gain control and multi-style training for robust small-footprint keyword spotting with deep neural networks., , , , and . ICASSP, page 4704-4708. IEEE, (2015)Auto-encoder bottleneck features using deep belief networks., , and . ICASSP, page 4153-4156. IEEE, (2012)A Better and Faster end-to-end Model for Streaming ASR., , , , , , , , , and 5 other author(s). ICASSP, page 5634-5638. IEEE, (2021)