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Listening Hard: New Tools for Monitoring Effort in Listening to Speech in Noise

  • Full or part time
  • Application Deadline
    Sunday, May 31, 2020
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Supervisor: Professor Simpson D.M.

Project description

Hearing speech is usually easy, but understanding speech in noise or in poor acoustic environments can be very challenging. This impacts people’s ability to communicate, especially over extended periods, impair their social interaction and their performance at work or at school. For hearing impaired listeners, some even with only mild impairment, this can become critical and exclude them from particular environments and activities, and degrade their general wellbeing.

However, our understanding of ‘Effort in Listening’ or ‘Ease of Listening’ remains limited, and current tools to quantify these constructs are not sufficiently robust and no ‘gold standard’ has emerged. The overarching aim of the current project is to find methods that can continuously monitor ‘effort’ in listening over extended periods, using a range of physiological signals (e.g. heart-rate, respiratory rate, skin impedance, pupil diameter etc.) and advanced multivariate signal analysis approaches. Our vision is that such methods will support the evidence-based design of future aided hearing devices (hearing aids, cochlear implants etc.), their fitting to improve ease of listening. The methods also aim to support the optimized design of acoustic environments and noise emissions for ease of communication. Monitoring effort continuously over extended periods, and not only after specific (brief) tasks, as is currently commonly done, will permit the assessment of fatigue and the effects of changing listening conditions, in a manner that is (relatively) unobtrusive and closer to ‘real-world’ challenges to listening to speech in noise.

The successful applicant for this PhD studentship will have a solid foundation in engineering or physical sciences, and experience in signal processing. A background in biomedical engineering, audiology or psychology would be advantageous, but is not essential. The project will involve both experimental work with human volunteers and the analysis of recorded data.
Context:

The research area ‘Effort in Listening’ has recently become a ‘hot topic’ in hearing science, and is very relevant to the wider field of acoustical engineering, including audio engineering and the optimization of noise emissions / noise reduction. Recent work has shown that measurement of ‘effort in listening’ is challenging, and that psychophysiological measures (the focus of the current proposal), are probably the most robust approach. Similar techniques are also used in other areas of ‘Human Factors’ research in the Faculty, and this interaction will contribute to enhancing collaboration, skills and expertise in the field. The main innovation in this project will be the investigation of individualized (personal) patterns of responses and their temporal evolution, the use of additional physiological measures (and their combination) and the monitoring of effort in listening when listeners may be fatiguing.

Entry Requirements
A very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent).

Closing date: applications should be received no later than 31 May 2020.

Funding: full tuition fees for EU/UK students plus for UK students, an enhanced stipend of £15,009 tax-free per annum for up to 3.5 years.

How To Apply

Applications should be made online here selecting “PhD Eng & Env (Full time)” as the programme. Please enter Prof Simpson under the proposed supervisor.

Applications should include:
Research Proposal
Curriculum Vitae
Two reference letters
Degree Transcripts to date
Apply online: https://www.southampton.ac.uk/courses/how-to-apply/postgraduate-applications.page

For further information please contact:

How good is research at University of Southampton in General Engineering?

FTE Category A staff submitted: 192.23

Research output data provided by the Research Excellence Framework (REF)

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