The 1st Clarity Prediction Challenge: A machine learning challenge for hearing aid intelligibility prediction

Barker, Jon; Akeroyd, Michael; J. Cox, Trevor; F. Culling, John; Firth, Jennifer; Graetzer, Simone; Griffiths, Holly; Harris, Lara; Naylor, Graham; Podwinska, Zuzanna; Porter, Eszter; Viveros Munoz, Rhoddy

Abstract

This paper reports on the design and outcomes of the 1st Clarity Prediction Challenge (CPC1) for predicting the intelligibility of hearing aid processed signals heard by individuals with a hearing impairment. The challenge was designed to promote the development of new intelligibility measures suitable for use in developing hearing aid algorithms. Participants were supplied with listening test data compromising 7233 responses from 27 individuals. Data was split between training and test sets in a manner that fostered a machine learning approach and allowed both closed-set (known listeners) and open-set (unseen listener/unseen system) evaluation. The paper provides a description of the challenge design including the datasets, the hearing aid algorithms applied, the listeners and the perceptual tests. The challenge attracted submissions from 15 systems. The results are reviewed and the paper summarises, compares and contrasts approaches.

• Presentation Conference Type: Conference Paper (published)
• Conference Name: Interspeech 2022
• Acceptance Date: Aug 8, 2022
• Publication Date: Sep 18, 2022
• Deposit Date: Aug 14, 2024
• Peer Reviewed: Peer Reviewed
• Pages: 3508-3512
• DOI: https://doi.org/10.21437/interspeech.2022-10821