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.

多模态特征的分类器可以准确区分变化言谈，持续言谈和跟随 / 中性言谈，在 Motivational Interviewing 中进行精确分类至关重要。通过对可公开获得的 AnnoMI 数据集进行注释，我们训练了一个使用文本、语调、面部表情和体态表达等多模态特征的模型，进一步发现了决策过程中最重要的感知模态，提供了不同感知模态在 Motivational Interviewing 对话中的相互作用的有价值的洞见。