Interpersonal Synchrony: A Survey of Evaluation Methods across Disciplines <s> FULLY AUTOMATIC MEASURES OF MOVEMENT SYNCHRONY <s> In conversation, when speech is followed by a backchannel, evidence of continued engagement by one's dialogue partner, that speech displays a combination of cues that appear to signal to one's interlocutor that a backchannel is appropriate. We term these cues back-channel-preceding cues (BPC)s, and examine the Columbia Games Corpus for evidence of entrainment on such cues. Entrainment, the phenomenon of dialogue partners becoming more similar to each other, is widely believed to be crucial to conversation quality and success. Our results show that speaking partners entrain on BPCs; that is, they tend to use similar sets of BPCs; this similarity increases over the course of a dialogue; and this similarity is associated with measures of dialogue coordination and task success. <s> BIB001 </s> Interpersonal Synchrony: A Survey of Evaluation Methods across Disciplines <s> FULLY AUTOMATIC MEASURES OF MOVEMENT SYNCHRONY <s> People in dialog use a rich set of nonverbal behaviors, including variations in the prosody of their utterances. Such behaviors, often emotion-related, call for appropriate responses, but today's spoken dialog systems lack the ability to do this. Recent work has shown how to recognize user emotions from prosody and how to express system-side emotions with prosody, but demonstrations of how to combine these functions to improve the user experience have been lacking. Working with a corpus of conversations with students about graduate school, we analyzed the emotional states of the interlocutors, utterance-by-utterance, using three dimensions: activation, evaluation, and power. We found that the emotional coloring of the speaker's utterance could be largely predicted from the emotion shown by her interlocutor in the immediately previous utterance. This finding enabled us to build Gracie, the first spoken dialog system that recognizes a user's emotional state from his or her speech and gives a response with appropriate emotional coloring. Evaluation with 36 subjects showed that they felt significantly more rapport with Gracie than with either of two controls. This shows that dialog systems can tap into this important level of interpersonal interaction using today's technology. <s> BIB002
To avoid tedious coding, automatic techniques can be used to capture pertinent social signals and assess movement synchrony in human-human interactions. The studies reviewed in this section aim to measure the degree of similarity between the dynamics of the nonverbal behaviors of dyadic partners. The goals of these studies are generally divisible into two categories: to compare the degree of synchrony under different conditions (e.g., with or without visual feedback) and to study the correlation between the degree of synchrony and an outcome variable (e.g., friendship, relationship quality). Consequently, these methods are mostly unsupervised in the sense that the measure of synchrony is not validated per se; rather, the ability of the measure to predict the outcome variable or to discriminate the different conditions is important. The methods described in this section were applied to adult-adult and child-adult interactions (Tables 1, 2 , and 3). We propose describing in detail the traditional steps of a computational model to assess synchrony ( Fig. 1) . Although our focus is mostly on the study of movement synchrony, many of the methods, issues, and findings are similar to the study of such subjects as entrainment or adaptation in spoken language interaction. For instance, Levitan et al. BIB001 studied global and local measures of entrainment in backchannel-preceding cues based on audio features (intonation, voice quality, pitch, intensity, duration) and its association with dialog coordination and task success. Beň uš et al.  studied the link between the alignment of turn-taking behavior and the achievement of pragmatic goals. They quantitatively measure the rhythm entrainment between speakers as the latency of the first pitch accent after a turn exchange divided by the rate of pitch accents in the utterance preceding the turn exchange. Finally, Acosta and Ward BIB002 proposed using a machine learning algorithm to predict the emotional coloring (valence, activation, power) of an utterance based on the emotional coloring of the previous utterance.