Clarity-2021 challenges: Machine learning challenges for advancing hearing aid processing
(3 minutes introduction)
| Simone Graetzer (University of Salford, UK), Jon Barker (University of Sheffield, UK), Trevor J. Cox (University of Salford, UK), Michael Akeroyd (University of Nottingham, UK), John F. Culling (Cardiff University, UK), Graham Naylor (University of Nottingham, UK), Eszter Porter (University of Nottingham, UK), Rhoddy Viveros Muñoz (Cardiff University, UK) |
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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.
