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 language | English (US) |
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Article number | 035207 |
Journal | JASA Express Letters |
Volume | 3 |
Issue number | 3 |
DOIs | |
State | Published - 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)