Modelling Paralinguistic Properties in Conversational Speech to Detect Bipolar Disorder and Borderline Personality Disorder
This work addresses a diagnostic problem for clinicians dealing with overlapping mental health conditions, but it appears incremental as it builds on existing feature-based detection methods.
The paper tackled the challenge of automatically detecting bipolar disorder (BD) and borderline personality disorder (BPD) by modeling verbal and non-verbal cues in interviews, and demonstrated superior performance of a signature-based model over statistical functions.
Bipolar disorder (BD) and borderline personality disorder (BPD) are two chronic mental health conditions that clinicians find challenging to distinguish based on clinical interviews, due to their overlapping symptoms. In this work, we investigate the automatic detection of these 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 it 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.