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Bipolar Disorder, a mood disorder with recurrent mania and depression, requires ongoing monitoring and specialty management. Current monitoring strategies are clinically-based, engaging highly specialized medical professionals who are becoming increasingly scarce. Automatic speech-based monitoring via smartphones has the potential to augment clinical monitoring by providing inexpensive and unobtrusive measurements of a patient’s daily life. The success of such an approach is contingent on the ability to successfully utilize “in-the-wild” data. However, most existing work on automatic mood detection uses datasets collected in clinical or laboratory settings. This study presents experiments in automatically detecting depression severity in individuals with Bipolar Disorder using data derived from clinical interviews and from personal conversations. We find that mood assessment is more accurate using data collected from clinical interactions, in part because of their highly structured nature. We demonstrate that although the features that are most effective in clinical interactions do not extend well to personal conversational data, we can identify alternative features relevant in personal conversational speech to detect mood symptom severity. Our results highlight the challenges unique to working with “in-the-wild” data, providing insight into the degree to which the predictive ability of speech features is preserved outside of a clinical interview.
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Emotion Recognition in the Real World: Passively Collecting and Estimating Emotions from Natural Speech Data of Individuals with Bipolar Disorder
Emotions provide critical information regarding a person's health and well-being. Therefore, the ability to track emotion and patterns in emotion over time could provide new opportunities in measuring health longitudinally. This is of particular importance for individuals with bipolar disorder (BD), where emotion dysregulation is a hallmark symptom of increasing mood severity. However, measuring emotions typically requires self-assessment, a willful action outside of one's daily routine. In this paper, we describe a novel approach for collecting real-world natural speech data from daily life and measuring emotions from these data. The approach combines a novel data collection pipeline and validated robust emotion recognition models. We describe a deployment of this pipeline that included parallel clinical and self-report measures of mood and self-reported measures of emotion. Finally, we present approaches to estimate clinical and self-reported mood measures using a combination of passive and self-reported emotion measures. The results demonstrate that both passive and self-reported measures of emotion contribute to our ability to accurately estimate mood symptom severity for individuals with BD.
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- Award ID(s):
- 2230172
- PAR ID:
- 10536393
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE Transactions on Affective Computing
- ISSN:
- 2371-9850
- Page Range / eLocation ID:
- 1 to 14
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/TAFFC.2024.3407683
