Challenges to Revolutionise Hearing Device Processing

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.

The aim of the project is to improve the lives for many of the eleven million people in the UK who have a hearing impairment. We will do this by making hearing aids and other devices more effective for processing speech in noisy conditions. Economic impact will come through decreasing the burden of hearing impairment, which for the UK was estimated to be £30 billion per year by The Ear Foundation. This cost includes factors such as increased healthcare costs, increased use of social services, loss of income due to greater levels of unemployment and reduced quality of life. Direct engagement through our project partner, The Hearing Industry Research Consortium is a route to getting our research exploited by the industry and therefore improving the design of hearing devices prescribed by the NHS or bought privately. Consumer expectations is a major reason for lack of use of hearing devices, and consequently we are partnering with Action on Hearing loss to facilitate impact activity with device users, and will also do public engagement.

Impact will be achieved by increasing the number of speech, signal processing and machine learning researchers considering hearing impairment in their algorithms. Two of our partners, Amazon and Honda Research Institute Europe, are involved because they include researchers who come from these communities. The increase in research activity by others will first be driven through a series of signal processing challenges. The open tools and databases that we will create for the challenges, will readily allow researchers to model the perception of speech in a wide variety of scenarios, and act as a catalyst for increased research and development. The Pathways to Impact outlines how we will raise awareness and disseminate the outcomes of the research through the other routes such as our workshops, tutorials at international international conferences and the media.