Speech signal analysis finds application in diverse settings, and biomedical speech signal analysis has been gaining increasing momentum in the last 15-20 years. In this talk, I will focus on signal processing algorithms to quantify these potentially useful characteristics, and draw on different examples from my research work. These include information fusion approaches to better estimate fundamental frequency in speech, capitalizing on novel approaches to extract clinically useful information from speech signals, and combining speech signal processing with machine learning techniques to develop robust, automated decision support tools assisting experts on their day-to-day praxis in the context of medical applications and forensic applications. I will highlight contemporary challenges and areas for further development including discussing our work on the Parkinson’s Voice Initiative where we collected more than 19,000 phonations from people across 7 countries.
Dr Athanasios Tsanas ('Thanasis'), BSc, BEng, MSc, DPhil (Oxon), SMIEEE, FHEA, FRSM
Thanasis studied Engineering and completed a DPhil (PhD) in Applied Mathematics at the University of Oxford (2012). He worked at the University of Oxford as a Research Fellow in Biomedical Engineering and Applied Mathematics (2012-2016), Stipendiary Lecturer in Engineering Science (2014-2016), and Lecturer in Statistical Research Methods (2016-2019). He is currently an Associate Professor in Data Science at the Usher Institute, Edinburgh Medical School, University of Edinburgh. He is Co-founder of the NHS Digital Academy leadership programme, where he leads the development and delivery of 'Clinical Decision Support and Actionable Data Analytics'. He received the Andrew Goudie award (top PhD student across all disciplines, St. Cross College, University of Oxford, 2011), the EPSRC Doctoral Prize award (2012), the young scientist award (MAVEBA, 2013), the EPSRC Statistics and Machine Learning award (2015), the BIOSTEC/Biosignals best paper award (2021), and won a ‘Best reviewer’ award from the IEEE Journal of Biomedical Health Informatics (2015) and an ‘Outstanding Reviewer’ award from the journal Computers in Biology and Medicine. He sits on the Editorial Boards of JMIR Mental Health, JMIR mHealth and uHealth, and Frontiers in Neurology. He is a Senior Member of IEEE, a Fellow of the Higher Education Academy, and a Fellow of the Royal Society of Medicine.
Indicative publications where I will draw material for the talk
A. Tsanas, S. Arora: Data-driven subtyping of Parkinson’s using acoustic analysis of sustained vowels and cluster analysis: findings in the Parkinson’s voice initiative study, Springer Nature Computer Science (accepted), 2022
S. Arora, A. Tsanas: Assessing Parkinson’s disease at scale using telephone-recorded speech: insights from the Parkinson’s voice initiative, Diagnostics, Vol. 11(1); e1892, 2021
S. Arora, C. Lo, M. Hu, A. Tsanas: Smartphone speech testing for symptom assessment in rapid eye movement sleep behavior disorder and Parkinson’s disease, IEEE Access, Vol. 9, pp. 44813-44824, 2021
A. Tsanas, M.A. Little, L.O. Ramig: Remote assessment of Parkinson’s disease symptom severity using the simulated cellular mobile telephone network, IEEE Access, Vol. 9, pp. 11024-11036, 2021
S. Arora, L. Baghai-Ravary, A. Tsanas: Developing a large scale population screening tool for the assessment of Parkinson’s disease using telephone-quality speech, Journal of Acoustical Society of America, Vol. 145(5), 2871-2884, 2019
A. Tsanas, M. Zañartu, M.A. Little, C. Fox, L.O. Ramig, G.D. Clifford: Robust fundamental frequency estimation in sustained vowels: detailed algorithmic comparisons and information fusion with adaptive Kalman filtering, Journal of the Acoustical Society of America, Vol. 135, pp. 2885-2901, 2014
A. Tsanas, M.A. Little, C. Fox, L.O. Ramig: Objective automatic assessment of rehabilitative speech treatment in Parkinson’s disease, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 22, pp. 181-190, 2014
A. Tsanas, M.A. Little, P.E. McSharry, J. Spielman, L.O. Ramig: Novel speech signal processing algorithms for high-accuracy classification of Parkinson’s disease, IEEE Transactions on Biomedical Engineering, Vol. 59, pp. 1264-1271, 2012
A. Tsanas: Accurate telemonitoring of Parkinson’s disease symptom severity using nonlinear speech signal processing and statistical machine learning, Ph.D. thesis, Oxford Centre for Industrial and Applied Mathematics, University of Oxford, 2012
Location: Attend in person at J577 or via Zoom, https://gu-se.zoom.us/j/66299274809?pwd=Yjc2ejc2VVhraXVJMmhWeWtOQ2NuUT09