Big Data for Speech and Language Processing

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2018 IEEE International Conference on Big Data (Big Data) Big Data for Speech and Language Processing Xuedong Huang Microsoft, USA Abstract: Amongst all creatures the human species stands unique in Darwin’s natural selection process. It is no exaggeration that speech and language helped to differentiate human intelligence from animal intelligence in the evolution process. The impact of big data and cloud to speech and language evolution is foundational to realize the society’s AI vision. This talk will review how Microsoft achieved human parity on both conversational speech recognition and news machine translation research tasks and highlight significant challenges remaining to make speech and language production services mainstream in our AI journey. Biography Dr. Xuedong Huang is a Microsoft Technical Fellow in AI and Research. He leads Microsoft’s Speech and Language Group. In 1993, Huang joined Microsoft to found the company’s speech technology group. As the general manager of Microsoft’s spoken language efforts, he helped to bring speech to the mass market by introducing SAPI to Windows in 1995 and Speech Server to the enterprise call center in 2004. He served as General Manager for MSR Incubation and Chief Architect for Bing and Ads. In 2015, he returned to AI and Research to lead the advanced technology group. In 2016, he led the team achieving a historical conversational speech recognition human parity milestone on the Switchboard task. He helped to advance AI across Microsoft’s whole AI Stack: Solutions: AI for customer support (Project Toronto), Voice Assistant Cortana, Microsoft Translator; APIs: Cognitive Services on Azure; Engines: Speech, Machine translation, Gesture, and NLP; Deep Learning infrastructure: Cognitive Toolkit (CNTK) and GPU Cluster (Project Philly) He was on the faculty of School of Computer Sciences at Carnegie Mellon University before joining Microsoft. He received Alan Newell research excellence leadership medal in 1992 and IEEE Best Paper Award in 1993. He was named the Asian American Engineer of the Year (2011), and Wired Magazine’s 25 Geniuses (2016). He holds over 100 patents and published over 100 papers & 2 books. He received his PhD, MS, and BS in Computer Science from the University of Edinburgh, Tsinghua University, and Hunan University respectively. He has been elected fellow of IEEE and ACM. 978-1-5386-5035-6/18/$31.00 ©2018 IEEE 2 Authorized licensed use limited to: Product Marketing IEL. Downloaded on October 30,2020 at 16:10:50 UTC from IEEE Xplore. Restrictions apply. .

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Semi-Continuous Hidden Markov Models for Speech Recognition
SEMI-CONTINUOUS HIDDEN MARKOV MODELS FOR SPEECH RECOGNITION Xuedong Huang Thesis submitted for the degree of Doctor of Philosophy University of Edinburgh 1989 DECLARATION This thesis, unless explicitly noted, was composed entirely by myself, and reports original work of my own execution. Xuedong Huang', B.Sc.' M)$c. 2 B.Sc. in Computer Science, Hunan University, 1982 2 M.Sc. in Computer Science and Technology, Tsinghua University, 1984 -1- ABSTRACT Hidden Markov models, which can be based on either discrete output probability distributions or continuous mixture output density functions, have been demonstrated as one of the most powerful statistical tools available for automatic speech recognition. In this thesis, a semi-continuous hidden Markov model, which is a very general model including both discrete and continuous mixture hidden Markov models as its special forms, is proposed. It is a model in which vector quantisation, the discrete hidden Markov model, and the continuous mixture hidden Markov model are unified. Based on the assumption that each vector quantisation codeword can be represented by a continuous probability density function, the semi-continuous output probability is then a combination of discrete model-dependent weighting coefficients with these continuous codebook probability density functions. In comparison to the conventional continuous mixture hidden Markov model, the semi-continuous hidden Markov model can maintain the modelling ability of large-mixture probability density functions. In addition, the number of free parameters and the computational complexity can be reduced because all of the probability density functions are tied together in the codebook. The semi- continuous hidden Markov model thus provides a good solution to the conflict between detailed acoustic modelling and insufficient training data.
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