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Details of Grant 

EPSRC Reference: EP/S031448/1
Title: Challenges to Revolutionise Hearing Device Processing
Principal Investigator: Barker, Professor J
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Amazon Hearing Industry Research Consortium Honda
RNID (Royal Natnl Inst for Deaf People)
Department: Computer Science
Organisation: University of Sheffield
Scheme: Standard Research
Starts: 01 January 2020 Ends: 31 October 2025 Value (£): 371,114
EPSRC Research Topic Classifications:
Biomechanics & Rehabilitation Music & Acoustic Technology
EPSRC Industrial Sector Classifications:
Manufacturing Communications
Related Grants:
EP/S031308/1 EP/S031324/1 EP/S030298/1
Panel History:
Panel DatePanel NameOutcome
02 May 2019 EPSRC ICT Prioritisation Panel May 2019 Announced
Summary on Grant Application Form
One in six people in the UK have a hearing impairment, and this number is certain to increase as the population ages. Yet only 40% of people who could benefit from hearing aids have them, and most people who have the devices don't use them often enough. A major reason for this low uptake and use, is the perception that hearing aids perform poorly.

Perhaps the most serious problem is hearing speech in noise. Even the best hearing aids struggle in such situations. This might be in the home, where the boiling kettle forces conversations with friends to stop, or maybe in a railway station, where noise makes it impossible to hear announcements. If such difficulties force the hearing impaired to withdraw from social situations, this increases the risk of loneliness and depression. Moreover, recent research suggests hearing loss is a risk factor for dementia. Consequently, improving how hearing devices deal with speech in noise has the potential to improve many aspects of health and well-being for an aging population. Making the devices more effective should increase the uptake and use of hearing aids.

Our approach is inspired by the latest science in speech recognition and synthesis. These are very active and fast moving areas of research, especially now with the development of voice interfaces like Alexa. But most of this research overlooks users who have a hearing impairment. There are innovative approaches being developed in speech technology and machine learning, which could be the basis for revolutionising hearing devices. But to get such radical advances needs more researchers to consider hearing impairments. To do this, we will run a series of signal processing competitions ("challenges"), which will deal with increasingly difficult scenarios of hearing speech in noise. Using such competitions is a proven technique for accelerating research, especially in the fields of speech technology and machine learning.

We will develop simulation tools, models and databases needed to run the challenges. These will also lower barriers that currently prevent speech researchers from considering hearing impairment. Data would include the results of listening tests that characterise how real people perceive speech in noise, along with a comprehensive characterisation of each test subject's hearing ability, because hearing aid processing needs to be personalised. We will develop simulators to create different listening scenarios. Models to predict how the hearing impaired perceive speech in noise are also needed. Such data and tools will form a test-bed to allow other researchers to develop their own algorithms for hearing aid processing in different listening scenarios. We will also challenge researchers to improve our models of perception.

The scientific legacy of the project will be improved algorithms for hearing aid processing; a test-bed that readily allows further development of algorithms, and more speech researchers considering the hearing abilities of the whole population.

Key Findings
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Potential use in non-academic contexts
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Date Materialised
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Further Information:  
Organisation Website: http://www.shef.ac.uk