Modelling Paralinguistic Properties in Conversational Speech to Detect Bipolar Disorder and Borderline Personality Disorder

TL;DR

This paper presents a novel signature-based approach to automatically detect bipolar disorder and borderline personality disorder from conversational speech, outperforming traditional statistical methods by modeling verbal and non-verbal cues.

Contribution

The study introduces a signature-transform technique for short-term feature modeling and compares it with high-level statistical functions, demonstrating improved detection performance.

Findings

Signature-based model outperforms statistical functions in detection accuracy.

Different feature sets uniquely characterize BD and BPD.

Modeling both verbal and non-verbal cues enhances detection.

Abstract

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.