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Bipolar disorder (BPD) is a chronic mental illness characterized by extrememood and energy changes from mania to depression. These changes drive behaviorsthat often lead to devastating personal or social consequences. BPD is managedclinically with regular interactions with care providers, who assess mood,energy levels, and the form and content of speech. Recent work has proposedsmartphones for monitoring mood using speech. However, these works do notpredict when to intervene. Predicting when to intervene is challenging becausethere is not a single measure that is relevant for every person: differentindividuals may have different levels of symptom severity considered typical.Additionally, this typical mood, or baseline, may change over time, making asingle symptom threshold insufficient. This work presents an innovativeapproach that expands clinical mood monitoring to predict when interventionsare necessary using an anomaly detection framework, which we call TemporalNormalization. We first validate the model using a dataset annotated forclinical interventions and then incorporate this method in a deep learningframework to predict mood anomalies from natural, unstructured, telephonespeech data. The combination of these approaches provides a framework to enablereal-world speech-focused mood monitoring.

When to Intervene: Detecting Abnormal Mood using Everyday Smartphone Conversations