In spoken dialogue analysis, the speech signal is a rich source of information. We explore in this paper how low level features of the speech signal, such as pitch, loudness, and speaking rate, can inform a model of student interaction in collaborative learning dialogues. For instance, can we observe the way that two people's manners of speaking change over time to model something like rapport? By detecting interaction qualities such as rapport, we can better support collaborative interactions, which have been shown to be highly conducive to learning. For this, we focus on one particular phenomenon of spoken conversation, known as acoustic-prosodic entrainment, where dialogue partners become more similar to each other in their pitch, loudness, or speaking rate during the course of a conversation. We examine whether acoustic-prosodic entrainment is present in a novel corpus of collaborative learning dialogues, how people appear to entrain, to what degree, and report on the acoustic-prosodic features which people entrain on the most. We then investigate whether entrainment can facilitate detection of rapport, a social quality of the interaction. We find that entrainment does correlate to rapport; speakers appear to entrain primarily by matching their prosody on a turn-by-turn basis, and pitch is the most significant acoustic-prosodic feature people entrain on when rapport is present.

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Conversational entrainment, a pervasive communication phenomenon in which dialogue partners adapt their behaviors to align more closely with one another, is considered essential for successful spoken interaction. While well-established in other disciplines, this phenomenon has received limited attention in the field of speech pathology and the study of communication breakdowns in clinical populations. The current study examined acoustic-prosodic entrainment, as well as a measure of communicative success, in three distinctly different dialogue groups: (i) healthy native vs. healthy native speakers (Control), (ii) healthy native vs. foreign-accented speakers (Accented), and (iii) healthy native vs. dysarthric speakers (Disordered). Dialogue group comparisons revealed significant differences in how the groups entrain on particular acoustic–prosodic features, including pitch, intensity, and jitter. Most notably, the Disordered dialogues were characterized by significantly less acoustic-prosodic entrainment than the Control dialogues. Further, a positive relationship between entrainment indices and communicative success was identified. These results suggest that the study of conversational entrainment in speech pathology will have essential implications for both scientific theory and clinical application in this domain.

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Disaster response agencies have started to incorporate social media as a source of fast-breaking information to understand the needs of people affected by the many crises that occur around the world. These agencies look for tweets from within the region affected by the crisis to get the latest updates of the status of the affected region. However only 1% of all tweets are "geotagged" with explicit location information. First responders lose valuable information because they cannot assess the origin of many of the tweets they collect. In this work we seek to identify non-geotagged tweets that originate from within the crisis region. Towards this, we address three questions: (1) is there a difference between the language of tweets originating within a crisis region and tweets originating outside the region, (2) what are the linguistic patterns that can be used to differentiate withinregion and outside-region tweets, and (3) for non-geotagged tweets, can we automatically identify those originating within the crisis region in real-time?