  Motivational Interviewing (MI) is an approach to therapy that emphasizes
collaboration and encourages behavioral change. To evaluate the quality of an
MI conversation, client utterances can be classified using the MISC code as
either change talk, sustain talk, or follow/neutral talk. The proportion of
change talk in a MI conversation is positively correlated with therapy
outcomes, making accurate classification of client utterances essential. In
this paper, we present a classifier that accurately distinguishes between the
three MISC classes (change talk, sustain talk, and follow/neutral talk)
leveraging multimodal features such as text, prosody, facial expressivity, and
body expressivity. To train our model, we perform annotations on the publicly
available AnnoMI dataset to collect multimodal information, including text,
audio, facial expressivity, and body expressivity. Furthermore, we identify the
most important modalities in the decision-making process, providing valuable
insights into the interplay of different modalities during a MI conversation.
