Despite the widespread use of Motivational Interviewing (MI), the underlying mechanisms of its success are still poorly understood [14], especially the link between client change talk and subsequent behavior change [14,69]. Previous research has identified two possible active components underlying MI efficacy: a relational component involving elements of the therapist-client dyad including the expression of empathy, and a technical component focused on the differential evocation of client behaviors such as change talk or what a client says about their commitment to change [57]. Current analyses of these components are limited to investigations pertaining to language only and restricted by expensive and arduous manual coding which, despite the time and efforts expended to achieve reliability, may still not be sufficiently sensitive or specific to adequately test the complex theoretical propositions espoused by MI theorists. Our project will address shortcomings of current MI coding systems by introducing a novel computational framework that leverages our recent advances in automatic verbal and nonverbal behavior analyses as well as multimodal machine learning. Our framework aims to jointly analyze verbal (i.e., what is being said), nonverbal (i.e., how something is said), and dyadic (i.e., in what interpersonal context something is said) behavior to better identify in-session change talk and sustain talk that is predictive of post-session alcohol use. We will leverage already collected and annotated audio data from two NIAAA- funded single-session MI randomized clinical trials to improve drinking behavior (N=91; N=158). We will disseminate our findings through an extensive collection of client and dyadic behaviors through our proposed Client and Dyadic Behavior Databases. In addition, we will validate the generalizability of our computational framework using seven additional NIAAA- and federally funded RCTs that used different MI protocols for different target populations.