Using clinician annotations to improve automatic speech recognition of stuttered speech

Peter Heeman, Rebecca Lunsford, Andy McMillin, J. Scott Yaruss

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

In treating people who stutter, clinicians often have their clients read a story in order to determine their stuttering frequency. As the client is speaking, the clinician annotates each disfluency. For further analysis of the client's speech, it is useful to have a word transcription of what was said. However, as these are realtime annotations, they are not always correct, and they usually lag where the actual disfluency occurred. We have built a tool that rescores a word lattice taking into account the clinician's annotations. In the paper, we describe how we incorporate the clinician's annotations, and the improvement over a baseline version. This approach of leveraging clinician annotations can be used for other clinical tasks where a word transcription is useful for further or richer analysis.

Original languageEnglish (US)
Pages (from-to)2651-2655
Number of pages5
JournalUnknown Journal
Volume08-12-September-2016
DOIs
StatePublished - 2016

Keywords

  • Automatic speech recognition
  • Disfluency counts
  • Stuttering
  • User-interface

ASJC Scopus subject areas

  • Language and Linguistics
  • Human-Computer Interaction
  • Signal Processing
  • Software
  • Modeling and Simulation

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