Modelling Paralinguistic Properties In Conversational Speech To Detect Bipolar Disorder And Borderline Personality Disorder

Bipolar disorder (BD) and borderline personality disorder (BPD) are two chronic mental health conditions the clinicians find challenging to distinguish based on clinical interviews, due to their overlapping symptoms. In this work, we investigate the automatic detection of the two conditions by modelling both verbal and non-verbal cues in a set of interviews. We propose a new approach of modelling short-term features with visibility-signature transform, and compare with widely used high-level statistical functions. We demonstrate the superior performance of our proposed signature-based model. Furthermore, we show the role of different sets of features in characterising BD and BPD.