  Cognitive Behavioral Therapy (CBT) is a goal-oriented psychotherapy for
mental health concerns implemented in a conversational setting with broad
empirical support for its effectiveness across a range of presenting problems
and client populations. The quality of a CBT session is typically assessed by
trained human raters who manually assign pre-defined session-level behavioral
codes. In this paper, we develop an end-to-end pipeline that converts speech
audio to diarized and transcribed text and extracts linguistic features to code
the CBT sessions automatically. We investigate both word-level and
utterance-level features and propose feature fusion strategies to combine them.
The utterance level features include dialog act tags as well as behavioral
codes drawn from another well-known talk psychotherapy called Motivational
Interviewing (MI). We propose a novel method to augment the word-based features
with the utterance level tags for subsequent CBT code estimation. Experiments
show that our new fusion strategy outperforms all the studied features, both
when used individually and when fused by direct concatenation. We also find
that incorporating a sentence segmentation module can further improve the
overall system given the preponderance of multi-utterance conversational turns
in CBT sessions.
