Robust auditory profiling: Improved data-driven method and profile definitions for better hearing rehabilitation
Keywords:hearing loss, data-driven profiling, hearing rehabilitation, hearing deficits, supra-threshold distortion
Currently, the clinical characterization of hearing deficits for hearing-aid fitting is based on the pure-tone audiogram only. This relies on the assumption that the audiogram can predict performance in complex, supra-threshold tasks. Sanchez-Lopez et al. (2018) hypothesized that the hearing deficits of a given listener, both at threshold and supra-threshold levels, result from two independent types of auditory distortions. The authors performed a data-driven analysis of two large datasets with results from several tests, which led to the identification of four auditory profiles. However, the definition of the two types of distortion was challenged by differences between the two datasets in terms of the tests and listeners considered. In the Better hEAring Rehabilitation (BEAR) project, a new dataset was generated with the aim of overcoming these limitations. A heterogeneous group of listeners was tested using measures of speech intelligibility, loudness perception, binaural processing abilities and spectro-temporal resolution. As a consequence, the auditory profiles of Sanchez-Lopez et al. (2018) were refined. The updated auditory profiles, together with the investigation of optimal hearing-aid compensation strategies, are expected to form a solid basis for improved hearing-aid fitting.
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