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An Auditory Model-Inspired Objective Speech Intelligibility Estimate for Audio Systems

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Compared with subjective tests, objective measures save time and money. This paper presents the implementation of a new algorithm for objective speech intelligibility, based on the modified rhyme test using real speech. An auditory-model inspired signal processing framework approach gathers word selection evidence in auditory filter bank correlations and then uses an auditory attention model to perform word selection. It has been shown to outperform popular measures in terms of Pearson correlation coefficient to the human intelligibility scores. A real-time version of this approach has been integrated into a versatile audio test and measurement system supporting a number of interfaces (different combinations of devices/channels/systems). Examples and measurement results will be presented to show the advantages of this approach.

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