• DocumentCode
    2053909
  • Title

    Non-intrusive speech intelligibility assessment

  • Author

    Sharma, Divya ; Naylor, Patrick A. ; Brookes, Mike

  • Author_Institution
    Nuance Commun. Inc., Marlow, UK
  • fYear
    2013
  • fDate
    9-13 Sept. 2013
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    We present NISI, a novel non-intrusive speech intelligibility assessment method based on feature extraction and a binary tree regression model. A training method using the intrusive STOI method to automatically label large quantities of speech data is presented and utilized. Our method is shown to predict speech intelligibility with an RMS error of 0.08 STOI on a test database of noisy speech.
  • Keywords
    feature extraction; mean square error methods; speech intelligibility; trees (mathematics); NISI method; RMS error; binary tree regression; feature extraction; noisy speech; nonintrusive speech intelligibility assessment; speech data; training method; Abstracts; Covariance matrices; Electronic mail; Measurement; Nickel; Silicides; Speech; Classification and Regression Trees; Data Driven; Speech Intelligibility; Speech Quality;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Signal Processing Conference (EUSIPCO), 2013 Proceedings of the 21st European
  • Conference_Location
    Marrakech
  • Type

    conf

  • Filename
    6811458
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2053909