Predicting speech intelligibility for people with a hearing loss: The clarity challenges

Objective speech intelligibility metrics are used to reduce the need for time consuming listening tests. They are used in the design of audio systems; room acoustics and signal processing algorithms. Most published speech intelligibility metrics have been developed using young adults with so-called 'normal hearing', and therefore do not work well for those with different hearing characteristics. One of the most common causes of aural diversity is sensorineural hearing loss. While partially restoring perception through hearing aids is possible, results are mixed. This has led to the Clarity Project, which is running an open series of Enhancement Challenges to improve the processing of speech-in-noise for hearing aids. To enable this, objective metrics of speech intelligibility are needed, which work from signals produced by hearing aids for diverse listeners. For this reason, Clarity is also running Prediction Challenges to improve speech intelligibility models. Competitors are given a set of audio signals produced by hearing aid algorithms, and challenged to predict how many words a listener with a particular hearing characteristic will achieve. Drawing on the learning from the challenge, we will outline what has been learnt about improving intelligibility metrics for those with a hearing impairment