Extending a computational model of auditory processing towards speech intelligibility prediction
A speech intelligibility model is presented based on the computational auditory signal processing and perception model (CASP; Jepsen et al., 2008). CASP has previously been shown to successfully predict psychoacoustic data obtained in normal-hearing (NH) listeners in a wide range of listening conditions. Moreover, CASP can be parametrized to account for data from individual hearing-impaired listeners (Jepsen and Dau, 2011). In this study, the CASP model was investigated as a predictor of speech intelligibility measured in NH listeners in conditions of additive noise, phase jitter, spectral subtraction and ideal binary mask processing.
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