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A Minimum Error Rate Pattern Recognition Approach to Speech Recognition., , , and . IJPRAI, 8 (1): 5-31 (1994)An artificial neural network approach to automatic speech processing., , and . Neurocomputing, (2014)An Information-Extraction Approach to Speech Processing: Analysis, Detection, Verification, and Recognition., and . Proceedings of the IEEE, 101 (5): 1089-1115 (2013)Exploiting context-dependency and acoustic resolution of universal speech attribute models in spoken language recognition., , , and . INTERSPEECH, page 2718-2721. ISCA, (2010)A hybrid HMM/DNN approach to keyword spotting of short words., and . INTERSPEECH, page 1574-1578. ISCA, (2013)High-resolution acoustic modeling and compact language modeling of language-universal speech attributes for spoken language identification., , , and . INTERSPEECH, page 992-996. ISCA, (2015)Rapid adaptation for deep neural networks through multi-task learning., , , , , and . INTERSPEECH, page 3625-3629. ISCA, (2015)A universal VAD based on jointly trained deep neural networks., , , , , and . INTERSPEECH, page 2282-2286. ISCA, (2015)Maximum-likelihood stochastic matching approach to non-linear equalization for robust speech recognition., , and . ICSLP, ISCA, (1996)Simultaneous ANN feature and HMM recognizer design using string-based minimum classification error (MCE) training., and . ICSLP, ISCA, (1996)