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

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.