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2020
Journal Article
Titel
Prediction of individual speech recognition performance in complex listening conditions
Abstract
This study examined how well individual speech recognition thresholds in complex listening scenarios could be predicted by a current binaural speech intelligibility model. Model predictions were compared with experimental data measured for seven normal-hearing and 23 hearing-impaired listeners who differed widely in their degree of hearing loss, age, as well as performance in clinical speech tests. The experimental conditions included two masker types (multi-talker or two-talker maskers), and two spatial conditions (maskers co-located with the frontal target or symmetrically separated from the target). The results showed that interindividual variability could not be well predicted by a model including only individual audiograms. Predictions improved when an additional individual ""proficiency factor"" was derived from one of the experimental conditions or a standard speech test. Overall, the current model can predict individual performance relatively well (except in conditions high in informational masking), but the inclusion of age-related factors may lead to even further improvements.