Automated prediction of speech intelligibility in noise for hearing aids

Abstract

According to a study by Action on Hearing Loss (2017a), 80% of people with hearing loss have difficulty understanding speech in the presence of background noise. Currently, we rely on behavioural speech-in-noise tests to determine and compare the efficacy of different hearing aids for improvement of speech intelligibility. If a sufficiently reliable automated prediction of intelligibility could be made available, the cost and complexity of testing new hearing aids could be reduced, and may allow bodies such as the NHS to compare device performance more efficiently. This thesis aims to evaluate several existing speech intelligibility prediction metrics by comparing their outputs against results from behavioural speech-in-noise tests. Behavioural speech-in-noise test scores from 21 normal hearing participants and speech intelligibility predictions from automated metrics were obtained for IEEE sentences (Institute of Electrical and Electronics Engineers, 1969) in stationary, speech-shaped background noise at signal-to-noise ratios from -8 to +3 dB, as processed by three different hearing aid models (currently prescribed by the NHS) with and without noise reduction settings enabled in addition to a control condition with no amplification and a low-cost amplifying device. All automated prediction metrics tested showed a broad increase in intelligibility with increasing signal-to-noise ratio. However, only one of the three automated metrics tested, the Hearing Aid Speech Perception Index (HASPI) (Kates and Arehart, 2014), was able to detect statistically significant differences between conditions which mirrored those seen in behavioural speech-in-noise test results. HASPI did, however, struggle to accurately predict the behavioural speech-in-noise scores for some specific hearing aid conditions and signal-to-noise ratios. Further investigations attempted to identify the main causes of HASPIs shortcomings including analysis of feature importance and implementation of a range of mapping and machine learning methods, the effects of differing stimulus types and the robustness of HASPIs component features in combination with features from alternative existing automated metrics. This thesis concludes that currently available automated metrics for speech intelligibility prediction are not fully capable of detecting differences between devices and settings, particularly between efficient noise-reduction programs and a low-cost amplifier. Whilst these metrics form an excellent basis for speech intelligibility prediction, further work is needed to develop existing metrics for use in comparing and tuning hearing aids and settings.

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