Objective measures of speech quality are highly desirable in benchmarking and monitoring the performance of hearing aids (HAs). Existing HA speech quality indices such as the hearing aid speech quality index (HASQI) are intrusive in that they require a properly time-aligned and frequency-shaped reference signal to predict the quality of HA output. Two new reference-free HA speech quality indices are proposed in this paper, based on a model that amalgamates perceptual linear prediction (PLP), hearing loss (HL) modeling, and machine learning concepts. For the first index, HL-modified PLP coefficients and their statistics were used as the feature set, which was subsequently mapped to the predicted quality scores using support vector regression (SVR). For the second index, HL-impacted gammatone auditory filterbank energies and their second-order statistics constituted the feature set, which was again mapped using SVR. Two databases involving HA recordings were collected and utilized for the evaluation of the robustness and generalizability of the two indices. Experimental results showed that the index based on the gammatone filterbank energies not only correlated well with HA quality ratings by hearing impaired listeners, but also exhibited robust performance across different test conditions and was comparable to the full-reference HASQI performance.

A non-intrusive objective speech quality and intelligibility measure tailored to hearing restoration instruments is proposed and evaluated in complex listening environments. The measure builds upon the previously-proposed “speech-to-reverberation modulation energy ratio” (SRMR) by incorporating hearing impairment percepts, such as hearing loss thresholds and altered modulation frequency selectivity. Performance is assessed using speech data corrupted by additive noise, reverberation, and noise-plus-reverberation which were subjectively rated by cochlear implant and hearing aid users. Experimental results show that the developed measures outperform the original SRMR metric for hearing impaired listeners and achieve performance levels inline with existing intrusive quality and intelligibility metrics, but with the advantage of not requiring access to a clean reference signal. As such, the measure may be used to develop qualityor intelligibility-aware speech enhancement algorithms for advanced hearing restoration instruments.

In speech enhancement applications, a validated metric of noise reduction performance is vital in the relative ranking of noise reduction algorithms and in enhancing the performance of a noise reduction algorithm. subjective scores of enhanced speech remain the yardstick for performance, but objective metrics that emulate subjective evaluations are preferred for cost- and time-effectiveness. In this paper, we analyze the performance of two objective methods for predicting the quality of enhanced speech. The first method employs the coherence-based speech intelligibility index, while the second method uses features derived from the Moore-Glasberg auditory model. In both cases, the features are mapped to a quality score using the Bayesian modeling approach. Results show that the combination of the auditory model-based feature set and the Bayesian modeling provides the best performance in predicting the quality scores of enhanced speech.