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

Abstract

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
Volume3
Issue number3
DOIs
StatePublished - Mar 2023

Keywords

  • 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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