Blind Listening Tests of Hearing Aids at Scale

[2606.26342] A Large-Scale Database and Predictive Model of Listener-Rated Ease of Speech Understanding in Commercial Hearing Aids

[Submitted on 24 Jun 2026]

Title:A Large-Scale Database and Predictive Model of Listener-Rated Ease of Speech Understanding in Commercial Hearing Aids

View a PDF of the paper titled A Large-Scale Database and Predictive Model of Listener-Rated Ease of Speech Understanding in Commercial Hearing Aids, by Andrew Sabin and 2 other authors

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Abstract:HearAdvisor aims to provide hearing-aid consumers with audio-performance metrics and recordings that reflect real listening experience. For speech-related metrics, HearAdvisor has historically used HASPIv2, a metric designed to predict objective intelligibility and validated primarily under simulated distortions. Its relationship to consumer-rated ease of understanding for commercial hearing aids is uncertain. Here we introduce a large-scale perceptual dataset and learned metric for listener-rated perceived benefit for speech understanding. Website visitors with self-reported hearing loss completed a blind, MUSHRA-inspired listening test in which they rated recordings of commercial hearing aids on a five-point "Ease of Understanding" scale. The dataset contains 151,608 ratings, 104,298 after quality screening, spanning 10,394 binaural acoustic-manikin recordings from 83 commercial products across 72 realistic acoustic scenes. To predict these ratings, we pass aided audio and a matched clean-speech reference through a frozen Whisper encoder, subtract their internal representations, and train a small MLP head on the resulting difference embedding. On devices held out of training, the learned metric substantially outperforms HASPIv2 at the scene level (overall r = 0.92 vs. 0.83; loud = 0.89 vs. 0.75; quiet = 0.79 vs. 0.58). In loud scenes, performance reaches the split-half reliability of the listener ratings; in quiet scenes, it approaches that ceiling. The model also responds sensibly to controlled gain and SNR manipulations. Together, the dataset and model provide a new way to predict listener-rated ease of speech understanding for real commercial hearing-aid recordings.

Comments: 6 pages, 3 figures

Subjects:

Audio and Speech Processing (eess.AS)

Cite as: arXiv:2606.26342 [eess.AS]

(or arXiv:2606.26342v1 [eess.AS] for this version)

https://doi.org/10.48550/arXiv.2606.26342

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Andrew Sabin [view email] [v1] Wed, 24 Jun 2026 19:34:56 UTC (117 KB)

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