2016 - ACM Fellow For contributions to spoken language processing.
Xuedong Huang focuses on Speech recognition, Artificial intelligence, Language model, Hidden Markov model and Natural language processing. His Speech recognition research is multidisciplinary, incorporating perspectives in Context, Process and Set. His study explores the link between Artificial intelligence and topics such as Pattern recognition that cross with problems in Reduction, Noise reduction and Noise.
His study focuses on the intersection of Language model and fields such as Acoustic model with connections in the field of Recurrent neural network, Test set, NIST and Theoretical computer science. His studies in Hidden Markov model integrate themes in fields like Hidden semi-Markov model, Markov process, Markov model, Variable-order Markov model and Vocabulary. His research investigates the link between Speech synthesis and topics such as Speech processing that cross with problems in Spoken language and Audio signal.
Xuedong Huang mainly focuses on Speech recognition, Artificial intelligence, Natural language processing, Hidden Markov model and Word error rate. Xuedong Huang has researched Speech recognition in several fields, including Word and Microphone. Much of his study explores Artificial intelligence relationship to Pattern recognition.
His work investigates the relationship between Hidden Markov model and topics such as Codebook that intersect with problems in Vector quantization. His Speech processing research focuses on Spoken language and how it connects with Human–computer interaction. His study on Perplexity is often connected to Cache language model as part of broader study in Language model.
Xuedong Huang spends much of his time researching Artificial intelligence, Natural language processing, Automatic summarization, Speech recognition and Language model. His study in the field of Utterance and Machine translation also crosses realms of Invariant and Parity. The concepts of his Natural language processing study are interwoven with issues in Graph, Knowledge graph, Identifier, User device and Transcription.
His Automatic summarization research also works with subjects such as
Xuedong Huang mostly deals with Artificial intelligence, Natural language processing, Automatic summarization, Speech recognition and Word error rate. His Artificial intelligence study combines topics in areas such as Contextual design and Conversation. His work on Language model and Machine translation as part of general Natural language processing research is often related to Parity, thus linking different fields of science.
His Automatic summarization study also includes fields such as
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Spoken Language Processing: A Guide to Theory, Algorithm, and System Development
Xuedong Huang;Alex Acero;Hsiao-Wuen Hon;Raj Reddy.
Hidden Markov Models for Speech Recognition
Xuedong Huang;Yasuo Ariki;Mervyn Jack.
Spoken Language Processing
Alex Acero;Xuedong Huang;Hsiao-Wuen Hon.
The Microsoft 2017 Conversational Speech Recognition System
W. Xiong;L. Wu;F. Alleva;J. Droppo.
international conference on acoustics, speech, and signal processing (2018)
The SPHINX-II Speech Recognition System: An Overview
Xuedong Huang;Fileno Alleva;Hsiao Hon;Mei Hwang.
Computer Speech & Language (1992)
Achieving Human Parity in Conversational Speech Recognition
Wayne Xiong;Jasha Droppo;Xuedong Huang;Frank Seide.
arXiv: Computation and Language (2016)
Achieving Human Parity on Automatic Chinese to English News Translation
Hany Hassan;Anthony Aue;Chang Chen;Vishal Chowdhary.
arXiv: Computation and Language (2018)
An Introduction to Computational Networks and the Computational Network Toolkit
Dong Yu;Adam Eversole;Mike Seltzer;Kaisheng Yao.
Semi-continuous hidden Markov models for speech signals
X. D. Huang;M. A. Jack.
Computer Speech & Language (1990)
Method and system of runtime acoustic unit selection for speech synthesis
Alejandro Acero;James L. Adcock;Xuedong D. Huang;Michael D. Plumpe.
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