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Automatic Affective Behaviour Monitoring through speech while preserving user’s privacy


   College of Medicine and Veterinary Medicine

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  Dr S Luz, Dr Fasih Haider  No more applications being accepted  Funded PhD Project (Students Worldwide)

Edinburgh United Kingdom Applied Mathematics Bioinformatics Biomedical Engineering Data Analysis Electronic Engineering Computer Science Software Engineering Statistics

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.

 

Eligibility:

Candidates will typically have an undergraduate degree in computer science, electrical engineering, physics, mathematics, or related subjects. Knowledge of and/or interest in signal processing, privacy-based machine learning, mobile application development and affective computing would be desirable.


Funding Notes

PhD's are fully funded with an above industry stipend for the full 4 year period.

The call is open to candidates of any nationality but funded places for overseas nationals will be strictly limited to 3 international students who can apply for the highly competitive ACRC Global Scholarship.

Application forms are now available here:
https://forms.office.com/Pages/ResponsePage.aspx?id=sAafLmkWiUWHiRCgaTTcYTowdNhupkBEnjWtstgAk6lURUU1SEVWUDJSM0s4RVVOSEQySU5LVEtOMS4u

Find more information on how to apply on the How to Apply section of our website:
https://www.ed.ac.uk/usher/advanced-care-research-centre/academy/how-to-apply

References

ACRC Academy Video:



Haider, F. and Luz, S., 2019. A System for Real-Time Privacy Preserving Data Collection for Ambient Assisted Living. In INTERSPEECH.
Haider, F., Pollak, S., Albert, P. and Luz, S., 2020. Emotion recognition in low-resource settings: An evaluation of automatic feature selection methods. Computer Speech & Language
Haider, F. and Luz, S., 2019, May. Attitude recognition using multi-resolution cochleagram features. In 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Nautsch, A., et Al., 2019. Preserving privacy in speaker and speech characterisation. Computer Speech & Language.
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