  Bipolar disorder (BPD) is a chronic mental illness characterized by extreme
mood and energy changes from mania to depression. These changes drive behaviors
that often lead to devastating personal or social consequences. BPD is managed
clinically with regular interactions with care providers, who assess mood,
energy levels, and the form and content of speech. Recent work has proposed
smartphones for monitoring mood using speech. However, these works do not
predict when to intervene. Predicting when to intervene is challenging because
there is not a single measure that is relevant for every person: different
individuals may have different levels of symptom severity considered typical.
Additionally, this typical mood, or baseline, may change over time, making a
single symptom threshold insufficient. This work presents an innovative
approach that expands clinical mood monitoring to predict when interventions
are necessary using an anomaly detection framework, which we call Temporal
Normalization. We first validate the model using a dataset annotated for
clinical interventions and then incorporate this method in a deep learning
framework to predict mood anomalies from natural, unstructured, telephone
speech data. The combination of these approaches provides a framework to enable
real-world speech-focused mood monitoring.
