title: Towards spoken clinical-question answering: evaluating and adapting automatic speech-recognition systems for spoken clinical questions.
abstract: OBJECTIVE
  To evaluate existing automatic speech-recognition (ASR) systems to measure their performance in interpreting spoken clinical questions and to adapt one ASR system to improve its performance on this task.
  
  
  METHODS
  The authors evaluated two well-known ASR systems on spoken clinical questions: Nuance Dragon (both generic and medical versions: Nuance Gen and Nuance Med) and the SRI Decipher (the generic version SRI Gen). The authors also explored language model adaptation using more than 4000 clinical questions to improve the SRI system's performance, and profile training to improve the performance of the Nuance Med system. The authors reported the results with the NIST standard word error rate (WER) and further analyzed error patterns at the semantic level.
  
  
  RESULTS
  Nuance Gen and Med systems resulted in a WER of 68.1% and 67.4% respectively. The SRI Gen system performed better, attaining a WER of 41.5%. After domain adaptation with a language model, the performance of the SRI system improved 36% to a final WER of 26.7%.
  
  
  CONCLUSIONS
  Without modification, two well-known ASR systems do not perform well in interpreting spoken clinical questions. With a simple domain adaptation, one of the ASR systems improved significantly on the clinical question task, indicating the importance of developing domain/genre-specific ASR systems.
authors.affiliation: Department of Health Sciences, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, USA.
authors.forename: Feifan
authors.initials: F
authors.lastname: Liu
authors.forename: Gokhan
authors.initials: G
authors.lastname: Tur
authors.forename: Dilek
authors.initials: D
authors.lastname: Hakkani-Tür
authors.forename: Hong
authors.initials: H
authors.lastname: Yu
mesh_terms: D057225:Data Mining; D020000:Decision Support Systems, Clinical; D006801:Humans; D009323:Natural Language Processing; D012660:Semantics; D012985:Software Design; D049250:Speech Recognition Software; D013673:Technology Assessment, Biomedical; D017432:Unified Medical Language System