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Modeling the effect of linguistic predictability on speech intelligibility prediction
Amin Edraki1, Wai-Yip Chan1, Daniel Fogerty2
1Department of Electrical and Computer Engineering, Queen's University, Kingston, Ontario K7L 3N6, Canada.
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.
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