Evaluation of a semi-supervised self-adjustment fine-tuning procedure for hearing aids.

OBJECTIVE: This study investigated the effects of different adjustment criteria and sound scenes on self-adjusted hearing-aid gain settings. Self-adjusted settings were evaluated for speech recognition in noise, perceived listening effort, and preference. DESIGN: This study evaluated a semi-supervised self-adjustment fine-tuning procedure that presents realistic everyday sound scenes in a laboratory environment, using a two-dimensional user interface, and enabling simultaneous changes in amplitude and spectral slope. While exploring the two-dimensional space of parameter settings, the hearing-aid users were instructed to optimise either listening comfort or speech understanding. STUDY SAMPLE: Twenty experienced hearing aid users (median age 69.5 years) were invited to participate in this study. RESULTS: Adjustment criterion and sound scenes had a significant effect on preferred gain settings. No differences in signal-to-noise ratios required for 50% speech intelligibility or in the perceived listening effort were observed between the adjusted settings of the two adjustment criteria. There was a preference for the self-adjusted settings over the prescriptive first fit. CONCLUSIONS: Listeners could reliably select their preferred gains to the two adjustment criteria and for different speech stimuli.

Automated Speech Audiometry for Integrated Voice Over Internet Protocol Communication Services.

PURPOSE: Problems in speech recognition are often apparent in telecommunication situations. For ecologically valid assessments of such conditions, it is important to quantify the impact of real environments including acoustic conditions at a far-end communication device and all paths of transmission degradation. This study presents an automated matrix sentence test procedure based on automatic speech recognition (ASR) integrated in a Voice over Internet Protocol (VoIP) infrastructure and compares the individual effects of transmission degradations with results from laboratory measurements. METHOD: Speech recognition thresholds (SRTs) were measured in 16 normal-hearing subjects in four test conditions: (a) a laboratory condition guided by a human experimenter, (b) a laboratory condition with reduced bandwidth and (c) additionally reduced headset quality to simulate typical communication systems, and (d) an automated, ASR-controlled adaptive test procedure over a real VoIP infrastructure. Errors of the ASR system were analyzed to show possible effects on measurement outcome Results: Measured SRTs showed a highly significant correlation (r = .93) between the fully automatic and "laboratory" conditions, with a constant bias of about 1 dB indicating a linear shift of the data without affecting the distribution around the mean. The individual impact of the different system degradations on SRTs could be quantified Conclusions: This study provides a proof of concept for automated ASR-based SRT measurements over VoIP systems for speech audiometric testing in real communication systems, as it produced results comparable to traditional laboratory settings for this group of 16 normal-hearing subjects. This makes VoIP services a promising candidate for speech audiometric testing in real communication systems.