4 papers
A multimodal Bayesian Network for symptom-level depression and anxiety prediction from voice and speech data
Agnes Norbury, George Fairs, Alexandra L. Georgescu +3
During psychiatric assessment, clinicians observe not only what patients report, but important nonverbal signs such as tone, speech rate, fluency, responsiveness, and body language…
Path Signature Representation of Patient-Clinician Interactions as a Predictor for Neuropsychological Tests Outcomes in Children: A Proof of Concept
Giulio Falcioni, Alexandra Georgescu, Emilia Molimpakis +3
This research report presents a proof-of-concept study on the application of machine learning techniques to video and speech data collected during diagnostic cognitive assessments…
Bayesian Networks for the robust and unbiased prediction of depression and its symptoms utilizing speech and multimodal data
Salvatore Fara, Orlaith Hickey, Alexandra Georgescu +3
Predicting the presence of major depressive disorder (MDD) using behavioural and cognitive signals is a highly non-trivial task. The heterogeneous clinical profile of MDD means tha…
Speech and the n-Back task as a lens into depression. How combining both may allow us to isolate different core symptoms of depression
Salvatore Fara, Stefano Goria, Emilia Molimpakis +1
Embedded in any speech signal is a rich combination of cognitive, neuromuscular and physiological information. This richness makes speech a powerful signal in relation to a range o…