Although age-related hearing loss affects 40% of 55 to 74 year-olds, the majority of adults who would benefit from hearing aids don’t use them. A key reason is simply that hearing aids don’t provide enough benefit.
Picking out speech from background noise is a critical problem even for the most sophisticated devices. The purpose of the Clarity Challenges is to catalyse new work to radically improve the speech intelligibility provided by hearing aids.
The series of challenges will consider increasingly complex listening scenarios. The first round, launching in January 2021, will focus on speech in indoor environments in the presence of a single interferer. It will begin with a challenge involving improving hearing aid processing. Future challenges on how to model speech-in-noise perception will be launched at a later date.
You will be provided with simulated scenes, each including a target speaker and interfering noise. For each scene, there will be signals that simulate those captured by a behind-the-ear hearing aid with three channels at each ear and those captured at the eardrum without a hearing aid present. The target speech will be a short sentence and the interfering noise will be either speech or domestic appliance noise.
The task will be to deliver a hearing aid signal processing algorithm that can improve the intelligibility of the target speaker for a specified hearing-impaired listener. Initially, entries will be evaluated using an objective speech intelligibility measure we will provide. Subsequently, up to twenty of the most promising systems will be evaluated by a panel of listeners.
We will provide a baseline system so that teams can choose to focus on individual components or to develop their own complete pipelines.
What will be provided
- Evaluation of the best entries by a panel of hearing-impaired listeners.
- Speech + interferer scenes for training and evaluation.
- An entirely new database of 10,000 spoken sentences
- Listener characterisations including audiograms and speech-in-noise testing.
- Software including tools for generating training data, a baseline hearing aid algorithm, a baseline model of hearing impairment, and a binaural objective intelligibility measure.
- January 2021 – Challenge launch and release of software and data
- April 2021 – Evaluation data released
- May 2021 – Submission deadline
- June-August 2021 – Listening test evaluation period
- September 2021 – Results announced at a Clarity Challenge Workshop in conjunction with Interspeech 2021
Challenge and workshop participants will be invited to contribute to a journal Special Issue on the topic of Machine Learning for Hearing Aid Processing that will be announced next year.
If you are interested in participating and wish to receive further information, please sign up.
If you have questions, contact us directly at firstname.lastname@example.org
Prof. Jon P. Barker, Department of Computer Science, University of Sheffield
Prof. Michael A. Akeroyd, Hearing Sciences, School of Medicine, University of Nottingham
Prof. Trevor J. Cox, Acoustics Research Centre, University of Salford
Prof. John F. Culling, School of Psychology, Cardiff University
Prof. Graham Naylor, Hearing Sciences, School of Medicine, University of Nottingham
Dr Simone Graetzer, Acoustics Research Centre, University of Salford
Dr Rhoddy Viveros Muñoz, School of Psychology, Cardiff University
Eszter Porter, Hearing Sciences, School of Medicine, University of Nottingham
Funded by the Engineering and Physical Sciences Research Council (EPSRC), UK.
Supported by RNID (formerly Action on Hearing Loss), Hearing Industry Research Consortium, Amazon TTS Research, Honda Research Institute Europe.
The image copyright is owned by the University of Nottingham.