# Identifying Mood Episodes Using Dialogue Features from Clinical Interviews

arXiv: https://arxiv.org/abs/1910.05115 · HTML: https://mlsanity.com/paper/1910.05115/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2019.

## Built on by (most cited first, 4 of 4)

- [Unsupervised Personalization of an Emotion Recognition System: The Unique Properties of the Externalization of Valence in Speech](https://mlsanity.com/paper/2201.07876/) (2022)
- [Modelling Paralinguistic Properties in Conversational Speech to Detect Bipolar Disorder and Borderline Personality Disorder](https://mlsanity.com/paper/2102.09607/) (2021)
- [A Multimodal Approach for Automatic Mania Assessment in Bipolar Disorder](https://mlsanity.com/paper/2112.09467/) (2021)
- [Can Conversational Temporal Dynamics Improve Depression Detection in Dyads? A Preliminary Investigation in Multi-Modality Perspectives](https://mlsanity.com/paper/2607.03744/) (2026)

## Abstract (arXiv)

Bipolar disorder, a severe chronic mental illness characterized by pathological mood swings from depression to mania, requires ongoing symptom severity tracking to both guide and measure treatments that are critical for maintaining long-term health. Mental health professionals assess symptom severity through semi-structured clinical interviews. During these interviews, they observe their patients' spoken behaviors, including both what the patients say and how they say it. In this work, we move beyond acoustic and lexical information, investigating how higher-level interactive patterns also change during mood episodes. We then perform a secondary analysis, asking if these interactive patterns, measured through dialogue features, can be used in conjunction with acoustic features to automatically recognize mood episodes. Our results show that it is beneficial to consider dialogue features when analyzing and building automated systems for predicting and monitoring mood.