Tavabi Leili, Borsari Brian, Stefanov Kalin, Woolley Joshua D, Soleymani Mohammad, Zhang Larry, Scherer Stefan
Institute for Creative Technologies, University of Southern California, Los Angeles, CA, USA.
VA hospital San Francisco, University of California San Francisco, San Francisco, CA, USA.
Proc ACM Int Conf Multimodal Interact. 2020 Oct;2020:406-413. doi: 10.1145/3382507.3418853.
Motivational Interviewing (MI) is defined as a collaborative conversation style that evokes the client's own intrinsic reasons for behavioral change. In MI research, the clients' attitude (willingness or resistance) toward change as expressed through language, has been identified as an important indicator of their subsequent behavior change. Automated coding of these indicators provides systematic and efficient means for the analysis and assessment of MI therapy sessions. In this paper, we study and analyze behavioral cues in client language and speech that bear indications of the client's behavior toward change during a therapy session, using a database of dyadic motivational interviews between therapists and clients with alcohol-related problems. Deep language and voice encoders, BERT and VGGish, trained on large amounts of data are used to extract features from each utterance. We develop a neural network to automatically detect the MI codes using both the clients' and therapists' language and clients' voice, and demonstrate the importance of semantic context in such detection. Additionally, we develop machine learning models for predicting alcohol-use behavioral outcomes of clients through language and voice analysis. Our analysis demonstrates that we are able to estimate MI codes using clients' textual utterances along with preceding textual context from both the therapist and client, reaching an F1-score of 0.72 for a speaker-independent three-class classification. We also report initial results for using the clients' data for predicting behavioral outcomes, which outlines the direction for future work.
动机性访谈（MI）被定义为一种协作性的对话风格，它能唤起客户自身行为改变的内在原因。在动机性访谈研究中，客户通过语言表达出的对改变的态度（愿意或抵触）已被确定为其后续行为改变的一个重要指标。对这些指标进行自动编码为分析和评估动机性访谈治疗环节提供了系统且高效的方法。在本文中，我们使用一个治疗师与有酒精相关问题的客户之间的二元动机性访谈数据库，研究和分析客户语言和言语中的行为线索，这些线索表明了客户在治疗环节中对改变的行为态度。在大量数据上训练的深度语言和语音编码器BERT和VGGish，被用于从每个话语中提取特征。我们开发了一个神经网络，使用客户和治疗师的语言以及客户的语音来自动检测动机性访谈代码，并证明了语义上下文在这种检测中的重要性。此外，我们开发了机器学习模型，通过语言和语音分析来预测客户的酒精使用行为结果。我们的分析表明，我们能够使用客户的文本话语以及来自治疗师和客户的先前文本上下文来估计动机性访谈代码，在一个独立于说话者的三类分类中达到了0.72的F1分数。我们还报告了使用客户数据预测行为结果的初步结果，这为未来的工作指明了方向。
