天美传媒

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Accessibility, Linguistics, Machine Learning, Natural Language, prosody, Speech Production, speech recognition, voice recognition

 is a  and a at the University of Illinois Urbana-Champaign. He is the William L. Everitt Faculty Scholar in ECE and holds affiliations in the Department of Speech and Hearing Science, Coordinated Science Lab, , and Department of Computer Science. He also leads the , a new research initiative to make voice recognition technology more useful for people with a range of diverse speech patterns and disabilities. 

Hasegawa-Johnson has been on the faculty at the University of Illinois since 1999. His research addresses automatic speech recognition with a focus on the mathematization of linguistic concepts. His group has developed mathematical models of concepts from linguistics including a rudimentary model of pre-conscious speech perception (the landmark-based speech recognizer), a model that interprets pronunciation variability by figuring out how the talker planned his or her speech movements (tracking of tract variables from acoustics, and of gestures from tract variables), and a model that uses the stress and rhythm of natural language (prosody) to disambiguate confusable sentences. Applications of his research include:

  • Speech recognition for talkers with cerebral palsy. The automatic system, suitably constrained, outperforms a human listener.
  • Provably correct unsupervised ASR, or ASR that can be trained using speech that has no associated text transcripts.

  • Equal Accuracy Ratio regularization: Methods that reduce the error rate gaps caused by gender, race, dialect, age, education, disability and/or socioeconomic class.

  • Automatic analysis of the social interactions between infant, father, mother, and older sibling during the first eighteen months of life.

Hasegawa-Johnson is currently Senior Area Editor of the journal IEEE Transactions on Audio, Speech and Language and a member of the ISCA Diversity Committee. He has published 308 peer-reviewed journal articles, patents, and conference papers in the general area of automatic speech analysis, including machine learning models of articulatory and acoustic phonetics, prosody, dysarthria, non-speech acoustic events, audio source separation, and under-resourced languages.

Education

  • Postdoctoral fellow, University of California at Los Angeles, 1996-1999
  • Ph.D., Massachusetts of Technology, 1996

  • M.S., Massachusetts Institute of Technology, 1989

Honors

  • 2023: Fellow of the International Speech Communication Association for contributions to knowledge-constrained signal generation
  • 2020: Fellow of the IEEE, for contributions to speech processing of under-resourced languages

  • 2011: Fellow of the Acoustical Society of America, for contributions to vocal tract and speech modeling

  • 2009: Senior Member of the Association for Computing Machinery

  • 2004: Member, Articulograph International Steering Committee; CLSP Workshop leader, "Landmark-Based Speech Recognition”, Invited paper

  • 2004: NAACL workshop on Linguistic and Higher-Level Knowledge Sources in Speech Recognition and Understanding

  • 2003: List of faculty rated as excellent by their students

  • 2002: NSF CAREER award

  • 1998: NIH National Research Service Award

Personal website:

CV:

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