Article,
Modeling the effect of linguistic predictability on speech intelligibility prediction
Affiliations
- [1] Queen's University [NORA names: Canada; America, North; OECD];
- [2] University of Illinois [NORA names: United States; America, North; OECD]
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.