Modeling the effect of linguistic predictability on speech intelligibility prediction

Amin Edraki, Wai Yip Geoffrey Chan, Daniel Fogerty, Jesper Jensen

Research output: Contribution to journalArticlepeer-review


Many existing speech intelligibility prediction (SIP) algorithms can only account for acoustic factors affecting speech intelligibility and cannot predict intelligibility across corpora with different linguistic predictability. To address this, a linguistic component was added to five existing SIP algorithms by estimating linguistic corpus predictability using a pre-trained language model. The results showed improved SIP performance in terms of correlation and prediction error over a mixture of four datasets, each with a different English open-set corpus.
Original languageEnglish (US)
Article number035207
JournalJASA Express Letters
Issue number3
StatePublished - Mar 2023


  • Auditory perception
  • hearing
  • acoustic distortion
  • audiometry
  • acoustic modeling
  • simulation and analysis
  • Speech intelligibility
  • cognitive science
  • probability theory
  • descriptive statistics
  • covariance and correlation

ASJC Scopus subject areas

  • Acoustics and Ultrasonics
  • Music
  • Arts and Humanities (miscellaneous)


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