The first Clarity Enhancement Challenge: Developing hearing aid algorithms for speech-in-noise

Graetzer, Simone, Akeroyd, Michael A., Barker, Jon, Cox, Trevor J., Culling, John F. ORCID: https://orcid.org/0000-0003-1107-9802, Firth, Jennifer, Naylor, Graham, Porter, Eszter and Viveros Munoz, Rhoddy 2026. The first Clarity Enhancement Challenge: Developing hearing aid algorithms for speech-in-noise. Computer Speech and Language 102 , 102021. 10.1016/j.csl.2026.102021

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Abstract

Hearing aid users frequently struggle to understand speech in noisy environments, negatively impacting their quality of life. Inspired by recent progress in speech technology through community-driven machine learning challenges, the Clarity project launched the first-ever Clarity Enhancement Challenge (CEC1). This challenge specifically addressed speech-in-noise enhancement for hearing aids, uniquely combining objective and subjective intelligibility evaluations to assess performance. Participants developed algorithms aimed at improving speech intelligibility in a simulated domestic environment featuring a target speaker and a stationary noise interferer—either competing speech or domestic appliances. Competitors were provided with an open-source dataset, comprising a novel 40-speaker British English corpus, realistic domestic noise samples, and a baseline hearing aid model with basic signal processing. This paper describes the design and outcomes of CEC1. Thirteen entries were evaluated objectively using the Modified Binaural Short-Time Objective Intelligibility metric (MBSTOI) and subjectively by a listening panel of hearing-impaired individuals. The majority of systems employed deep neural networks (DNNs), classical beamforming, or a combination of both. Results showed significant intelligibility gains over the baseline, particularly for systems combining adaptive beamforming with neural network-based noise reduction. However, algorithms optimised directly for MBSTOI scores did not always translate to real-world listening benefits, highlighting the critical importance of perceptual evaluation in assessing intelligibility. These findings underscore the potential of machine-learning-driven approaches for enhancing hearing aid performance and set a foundation for future challenges addressing dynamic and more realistic auditory scenarios.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Psychology
Publisher:	Elsevier
ISSN:	0885-2308
Date of First Compliant Deposit:	20 July 2026
Date of Acceptance:	19 June 2026
Last Modified:	20 Jul 2026 15:11
URI:	https://orca.cardiff.ac.uk/id/eprint/188328

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