Objective Quality and Intelligibility Prediction for Users of Assistive Listening Devices: Advantages and limitations of existing tools


This article presents an outline of 12 existing objective speech quality and intelligibility prediction tools. Two categories of algorithms are presented?intrusive and nonintrusive?with the previous requiring the utilization of a reference signal, whereas the latter does not. Investigated metrics include each those developed for traditional hearing (NH) listeners, along with those tailored particularly for hearing impaired (HI) listeners who are users of assistive listening devices [i.e., hearing aids (HAs) and cochlear implants (CIs)]. Representative examples of those optimized for HI listeners embrace the speech-to-reverberation modulation energy ratio (SRMR), tailored to HAs (SRMR-HA) and to CIs (SRMR-CI); the modulation spectrum space (ModA); the HA speech quality (HASQI) and perception indices (HASPI); and also the perception-model-primarily based quality prediction technique for hearing impairments (PEMO-Q-HI). The objective metrics are tested on 3 subjectively rated speech knowledge sets covering reverberation-alone, noise-alone, and reverberation-and-noise degradation conditions, as well as degradations resultant from nonlinear frequency compression and totally different speech enhancement methods. The advantages and limitations of every live are highlighted and recommendations are given for suggested uses of the various tools underneath specific environmental and processing conditions.

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