  In this work, we compare different neural topic modeling methods in learning
the topical propensities of different psychiatric conditions from the
psychotherapy session transcripts parsed from speech recordings. We also
incorporate temporal modeling to put this additional interpretability to action
by parsing out topic similarities as a time series in a turn-level resolution.
We believe this topic modeling framework can offer interpretable insights for
the therapist to optimally decide his or her strategy and improve psychotherapy
effectiveness.
