Clarity : machine learning challenges to revolutionise hearing device processing

Graetzer, SN; Akeroyd, M; Barker, J; Cox, TJ; Culling, J; Naylor, G; Porter, E; Viveros Munoz, R

Abstract

In the Clarity project, we will run a series of machine learning challenges to revolutionise speech processing for hearing devices. Over five years, there will be three paired challenges. Each pair will consist of a competition focussed on hearing-device processing (“enhancement”) and another focussed on speech perception modelling (“prediction”). The enhancement challenges will deliver new and improved approaches for hearing device signal processing for speech. The parallel prediction challenges will develop and improve methods for predicting speech intelligibility and quality for hearing impaired listeners. To facilitate the challenges, we will generate openaccess datasets, models and infrastructure. These will include: (1) tools for generating realistic test/training materials for different listening scenarios; (2) baseline models of hearing impairment; (3) baseline models of hearing-device processing; (4) baseline models of speech perception and (5) databases of speech perception in noise. The databases will include the results of listening tests that characterise how hearing-impaired listeners perceive speech in noise. We will also provide a comprehensive characterisation of each listeners hearing ability. The provision of open-access datasets, models and infrastructure will allow other researchers to develop algorithms for speech and hearing aid processing. In addition, it will lower barriers that prevent researchers from considering hearing impairment. In round one, speech will occur in the context of a living room, i.e., a moderately reverberant room with minimal (non-speech) background noise. Entries can be submitted to either the enhancement or prediction challenges, or both. We expect to open the beta version of round one in October for a full opening in November 2020, a closing date in June 2021 and results in October 2021. This Engineering and Physical Sciences Research Council (EPSRC) funded project involves researchers from the Universities of Sheffield, Salford, Nottingham and Cardiff in conjunction with the Hearing Industry Research Consortium, Action on Hearing Loss, Amazon, and Honda. To register interest in the challenges, go to www.claritychallenge.org/.

Citation

Graetzer, S., Akeroyd, M., Barker, J., Cox, T., Culling, J., Naylor, G., …Viveros Munoz, R. Clarity : machine learning challenges to revolutionise hearing device processing. Presented at e-Forum Acusticum 2020, Online

• Presentation Conference Type: Speech
• Conference Name: e-Forum Acusticum 2020
• Conference Location: Online
• End Date: Dec 11, 2020
• Publication Date: Dec 7, 2020
• Deposit Date: Feb 5, 2021
• Publicly Available Date: Feb 5, 2021
• DOI: https://doi.org/10.48465/fa.2020.0198
• Publisher URL: https://hal.archives-ouvertes.fr/hal-03234191
• Related Public URLs: https://hal.archives-ouvertes.fr/
• Additional Information: Access Information : This paper also appears in the ArXiv pre-print server, available at: https://arxiv.org/abs/2006.11140; Event Type : Conference