Funded PhD Position at University of Edinburgh

 

PhD Position: Automatic Affective Behaviour Monitoring through speech and/or
multimodal means while preserving user’s privacy

 

For details please visit: 

https://www.findaphd.com/phds/project/automatic-affective-behaviour-monitori
ng-through-speech-while-preserving-user-s-privacy/?p125956

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About the Project

The Advanced Care Research Centre at the University of Edinburgh is a new
£20m interdisciplinary research collaboration aiming to transform later life
with person centred integrated care

The vision of the ACRC is to play a vital role in addressing the Grand
Challenge of ageing by transformational research that will support the
functional ability of people in later life so they can contribute to their
own welfare for longer. With fresh and diverse thinking across
interdisciplinary perspectives our academy students will work to creatively
embed deep understanding, data science, artificial intelligence, assistive
technologies and robotics into systems of health and social care supporting
the independence, dignity and quality-of-life of people living in their own
homes and in supported care environments.

The ACRC Academy will equip future leaders to drive society’s response to
the challenges of later life care provision; a problem which is growing in
scale, complexity and urgency. Our alumni will become leaders in across a
diverse range of pioneering and influential roles in the public, private and
third sectors.

Automatic affect recognition technologies can monitor a person’s mood and
mental health by processing verbal and non-verbal cues extracted from the
person’s speech. However, the speech signal contains biometric and other
personal information which can, if improperly handled, threaten the
speaker’s privacy. Hence there is a need for automatic inference and
monitoring methods that preserve privacy for speech data in terms of
collection, training of machine learning models and use of such models in
prediction. This project will focus on research, implementation and
assessment of solutions for handling of speech data in the user’s own
environment while protecting their privacy. We are currently studying the
use of speech in healthy ageing and care in combination with IoT/Ambient
Intelligence technologies in a large research project. This project will
build on our research in this area.

 

The goals of this PhD project are:

*	to establish and assess user privacy requirements,
*	to devise privacy-preserving automatic affect recognition methods,
*	to develop speech data collection methods and tools for
privacy-sensitive contexts, and
*	to evaluate these methods with respect to performance and privacy
preservation requirements.

 

Training outcomes include machine learning methods for inference of mental
health status, privacy-preserving machine learning and signal processing,
and applications of such methods in elderly care.