Machine Learning Challenges to Revolutionise Hearing Devices

One in six people in the UK has a hearing impairment, and this number will increase as the population ages. Yet only 40% of people who could benefit from hearing aids have them, and most people who have the devices don’t use them often enough. A major reason for this low uptake and use is the perception that hearing aids perform poorly.

We are organising a series of machine learning challenges to enhance hearing-aid signal processing and to better predict how people perceive speech-in-noise.

Prediction Challenge: Now Live!

The first Prediction Challenge (CPC1) launched 16th November 2021. More details here. This challenge is about predicting the speech intelligibility score when listeners listened to speech-in-noise processed by hearing aids, and told us what words they heard.

Enhancement challenge

The first Enhancement Challenge (CEC1) was about enhancing the processing of hearing aid signals to improve the intelligibility of speech in noise. The scenario was a living room, one target talker and one interfering noise source. The challenge is now closed. The challenge workshop was held on the 16th and 17th September 2021. You can find out more about the workshop, and find links to papers, slides and videos of presentations here.