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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. 

Suicide is a serious public health concern in the U.S., taking the lives of over 47,000 people in 2017. Early detection of suicidal ideation is key to prevention. One promising approach to symptom monitoring is suicidal speech prediction, as speech can be passively collected and may indicate changes in risk. However, directly identifying suicidal speech is difficult, as characteristics of speech can vary rapidly compared with suicidal thoughts. Suicidal ideation is also associated with emotion dysregulation. Therefore, in this work, we focus on the detection of emotion from speech and its relation to suicide. We introduce the Ecological Measurement of Affect, Speech, and Suicide (EMASS) dataset, which contains phone call recordings of individuals recently discharged from the hospital following admission for suicidal ideation or behavior, along with controls. Participants self-report their emotion periodically throughout the study. However, the dataset is relatively small and has uncertain labels. Because of this, we find that most features traditionally used for emotion classification fail. We demonstrate how outside emotion datasets can be used to generate more relevant features, making this analysis possible. Finally, we use emotion predictions to differentiate healthy controls from those with suicidal ideation, providing evidence for suicidal speech detection using emotion. 

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.