The PRIORI Emotion Dataset: Linking Mood to Emotion Detected In-the-Wild

TL;DR

This paper introduces the PRIORI Emotion Dataset collected from everyday speech to improve mood monitoring in bipolar disorder through emotion recognition, demonstrating significant correlations between predicted emotion and mood states.

Contribution

It presents a new in-the-wild emotion dataset, baseline emotion recognition results, and evidence linking emotion prediction to mood state monitoring in bipolar disorder.

Findings

Emotion recognition baselines achieved PCC of 0.71 (activation) and 0.41 (valence).

Significant correlation found between predicted emotion and mood states.

The dataset enables future research in emotion-based mood monitoring.

Abstract

Bipolar Disorder is a chronic psychiatric illness characterized by pathological mood swings associated with severe disruptions in emotion regulation. Clinical monitoring of mood is key to the care of these dynamic and incapacitating mood states. Frequent and detailed monitoring improves clinical sensitivity to detect mood state changes, but typically requires costly and limited resources. Speech characteristics change during both depressed and manic states, suggesting automatic methods applied to the speech signal can be effectively used to monitor mood state changes. However, speech is modulated by many factors, which renders mood state prediction challenging. We hypothesize that emotion can be used as an intermediary step to improve mood state prediction. This paper presents critical steps in developing this pipeline, including (1) a new in the wild emotion dataset, the PRIORI Emotion Dataset, collected from everyday smartphone conversational speech recordings, (2) activation/valence emotion recognition baselines on this dataset (PCC of 0.71 and 0.41, respectively), and (3) significant correlation between predicted emotion and mood state for individuals with bipolar disorder. This provides evidence and a working baseline for the use of emotion as a meta-feature for mood state monitoring.