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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)A Comparison of End-to-End Models for Long-Form Speech Recognition., , , , , , , , , and 4 other author(s). ASRU, page 889-896. IEEE, (2019)Scaling Up Deliberation For Multilingual ASR., , and . SLT, page 771-776. IEEE, (2022)Application specific loss minimization using gradient boosting., , , and . ICASSP, page 4880-4883. IEEE, (2011)Deep Learning for Audio Signal Processing., , , , , and . CoRR, (2019)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)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)