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

arXiv: https://arxiv.org/abs/2102.09607 · HTML: https://mlsanity.com/paper/2102.09607/

## Key facts

- Cited by 1 papers in the map (data through 7 Oct 2026).
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 18 Feb 2021.

## Builds on (most cited first, 6 of 6)

- [A Primer on the Signature Method in Machine Learning](https://mlsanity.com/paper/1603.03788/) (2016)
- [Deep Signature Transforms](https://mlsanity.com/paper/1905.08494/) (2019)
- [End-to-end Domain-Adversarial Voice Activity Detection](https://mlsanity.com/paper/1910.10655/) (2019)
- [Learning to Detect Bipolar Disorder and Borderline Personality Disorder with Language and Speech in Non-Clinical Interviews](https://mlsanity.com/paper/2008.03408/) (2020)
- [Identifying Mood Episodes Using Dialogue Features from Clinical Interviews](https://mlsanity.com/paper/1910.05115/) (2019)
- [Signature features with the visibility transformation](https://mlsanity.com/paper/2004.04006/) (2020)

## Built on by (most cited first, 1 of 1)

- [Sig-Networks Toolkit: Signature Networks for Longitudinal Language Modelling](https://mlsanity.com/paper/2312.03523/) (2023)

## Abstract (arXiv)

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.