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.

This paper reports on the design and outcomes of the 2nd Clarity Prediction Challenge (CPC2) for predicting the intelligibility of hearing aid processed signals heard by individuals with a hearing impairment. The challenge was designed to promote new approaches for estimating the intelligibility of hearing aid signals that can be used in future hearing aid algorithm development. It extends an earlier round (CPC1, 2022) in a number of critical directions, including a larger dataset coming from new speech intelligibility listening experiments, a greater degree of variability in the test materials, and a design that requires prediction systems to generalise to unseen algorithms and listeners. This paper provides a full description of the new publicly available CPC2 dataset, the CPC2 challenge design, and the baseline systems. The challenge attracted 12 systems from 9 research teams. The systems are reviewed, their performance is analysed and conclusions are presented, with reference to the progress made since the earlier CPC1 challenge. In particular, it is seen how reference-free, non-intrusive systems based on pre-trained large acoustic models can perform well in this context.

This paper reports on the design and outcomes of the 2nd Clarity Enhancement Challenge (CEC2), a challenge for stimulating novel approaches to hearing-aid speech intelligibility enhancement. The challenge was for a listener attending to a target speaker in a noisy, domestic environment. The challenge extends the previous edition, CEC1, in a number of key respects: scenes have multiple interferers including speech, noise and music; ambisonics are used to model listener head movement; target speaker identity is provided to encourage speaker extraction approaches. Systems are evaluated both via the HASPI intelligibility metric and with listening tests using a panel of hearing-impaired listeners. The paper reviews the 18 systems that were submitted describing them in terms of their enhancement and amplification stages. HASPI is seen to be a good predictor of listener performance. The top system, using carefully engineered neural approaches, produces highly intelligible signals for complex scenes with SNRs down to -12 dB while obeying the challenges 5 ms latency constraint.

This paper reports on the design and outcomes of the ICASSP SP Clarity Challenge: Speech Enhancement for Hearing Aids. The scenario was a listener attending to a target speaker in a noisy, domestic environment. There were multiple interferers and head rotation by the listener. The challenge extended the second Clarity Enhancement Challenge (CEC2) by fixing the amplification stage of the hearing aid; evaluating with a combined metric for speech intelligibility and quality; and providing two evaluation sets, one based on simulation and the other on real-room measurements. Five teams improved on the baseline system for the simulated evaluation set, but the performance on the measured evaluation set was much poorer. Investigations are on-going to determine the exact cause of the mismatch between the simulated and measured data sets. The presence of transducer noise in the measurements, lower order Ambisonics harming the ability for systems to exploit binaural cues and the differences between real and simulated room impulse responses are suggested causes.

In recent years, rapid advances in speech technology have been made possible by machine learning challenges such as CHiME, REVERB, Blizzard, and Hurricane. In the Clarity project, the machine learning approach is applied to the problem of hearing aid processing of speech-in-noise, where current technology in enhancing the speech signal for the hearing aid wearer is often ineffective. The scenario is a (simulated) cuboid-shaped living room in which there is a single listener, a single target speaker and a single interferer, which is either a competing talker or domestic noise. All sources are static, the target is always within ±30° azimuth of the listener and at the same elevation, and the interferer is an omnidirectional point source at the same elevation. The target speech comes from an open source 40-speaker British English speech database collected for this purpose. This paper provides a baseline description of the round one Clarity challenges for both enhancement (CEC1) and prediction (CPC1). To the authors’ knowledge, these are the first machine learning challenges to consider the problem of hearing aid speech signal processing.