Robustness of the Hearing Aid Speech Quality Index (HASQI)

Objective measures of speech quality have been the subject of significant prior work, particularly in the areas of speech codecs and communication channels for normal-hearing listeners. One of the primary concerns of researchers in this area is how these metrics generalize to datasets or listener studies which are “unknown” to the measures.

Another growing concern is how these metrics perform for the hearing-impaired community. Researchers working with the this community need to be able to predict how hearing-impaired listeners will perceive the quality of speech, as well as how they will perceive the quality of speech processed specifically by hearing aids.

A relatively recent metric, the Hearing Aid Speech Quality Index (HASQI), is a model-based objective measure of quality developed in the context of hearing aids for normal-hearing and hearing-impaired listeners (Kates & Arehart, Journal of the Audio Engineering Society, 2010). As such, HASQI makes substantial progress on some of the generalization issues.

However, HASQI has not been tested thus far on any datasets other than the one on which it was trained. The objective of this study is to demonstrate the robustness of HASQI in predicting subjective quality. We use an “unknown” dataset of noisy speech processed by noise suppression algorithms, along with a corresponding set of subjective quality scores from normal-hearing listeners, to demonstrate HASQI's prediction performance.

Furthermore, we compare HASQI's performance with that of several other objective measures in order to provide a point of reference.