Conversational interaction is a dynamic activity in which participants engage in the construction of meaning and in establishing and maintaining social relationships. Lexical and prosodic accommodation have been observed in many studies as contributing importantly to these dimensions of social interaction. However, while previous works have considered accommodation mechanisms at global levels (for whole conversations, halves and thirds of conversations), this work investigates their evolution through repeated analysis at time intervals of increasing granularity to analyze the dynamics of alignment in a spoken language corpus. Results show that the levels of both prosodic and lexical accommodation fluctuate several times over the course of a conversation.

International audience

This article presents experiments on automatic detection of laughter and fillers, two of the most important nonverbal behavioral cues observed in spoken conversations. The proposed approach is fully automatic and segments audio recordings captured with mobile phones into four types of interval: laughter, filler, speech and silence. The segmentation methods rely not only on probabilistic sequential models (in particular Hidden Markov Models), but also on Statistical Language Models aimed at estimating the a-priori probability of observing a given sequence of the four classes above. The experiments are speaker independent and performed over a total of 8 hours and 25 minutes of data (120 people in total). The results show that F1scores up to 0.64 for laughter and 0.58 for fillers can be achieved.

This paper reports the results of an exploratory study into inadvertent grip pressure changes on mobile devices with a focus on the differences between static lab-based and mobile walking environments. The aim of this research is to inform the design of more robust pressure input techniques that can accommodate dynamic mobile usage. The results of the experiment show that there are significant differences in grip pressure in static and walking conditions with high levels of pressure variation in both. By combining the pressure data with accelerometer data, we show that grip pressure is closely related to user movement.

Roles are a key aspect of social interactions, as they contribute to the overall predictability of social behavior (a necessary requirement to deal effectively with the people around us), and they result in stable, possibly machine-detectable behavioral patterns (a key condition for the application of machine intelligence technologies). This paper proposes an approach for the automatic recognition of roles in conversational broadcast data, in particular, news and talk shows. The approach makes use of behavioral evidence extracted from speaker turns and applies conditional random fields to infer the roles played by different individuals. The experiments are performed over a large amount of broadcast material (around 50 h), and the results show an accuracy higher than 85%.

This paper proposes an approach for the automatic recognition of roles in settings like news and talk-shows, where roles correspond to specific functions like Anchorman, Guest or Interview Participant. The approach is based on purely nonverbal vocal behavioral cues, including who talks when and how much (turn-taking behavior), and statistical properties of pitch, formants, energy and speaking rate (prosodic behavior). The experiments have been performed over a corpus of around 50 hours of broadcast material and the accuracy, percentage of time correctly labeled in terms of role, is up to 89%. Both turn-taking and prosodic behavior lead to satisfactory results. Furthermore, on one database, their combination leads to a statistically significant improvement.

Automatic analysis of social interactions attracts increasing attention in the multimedia community. This letter considers one of the most important aspects of the problem, namely the roles played by individuals interacting in different settings. In particular, this work proposes an automatic approach for the recognition of roles in both production environment contexts (e.g., news and talk-shows) and spontaneous situations (e.g., meetings). The experiments are performed over roughly 90 h of material (one of the largest databases used for role recognition in the literature) and show that the recognition effectiveness depends on how much the roles influence the behavior of people. Furthermore, this work proposes the first approach for modeling mutual dependences between roles and assesses its effect on role recognition performance.

This paper presents an approach for the recognition of roles in multiparty recordings. The approach includes two major stages: extraction of Social Affiliation Networks (speaker diarization and representation of people in terms of their social interactions), and role recognition (application of discrete probability distributions to map people into roles). The experiments are performed over several corpora, including broadcast data and meeting recordings, for a total of roughly 90 hours of material. The results are satisfactory for the broadcast data (around 80 percent of the data time correctly labeled in terms of role), while they still must be improved in the case of the meeting recordings (around 45 percent of the data time correctly labeled). In both cases, the approach outperforms significantly chance.

This paper presents experiments on the automatic recognition of roles in meetings. The proposed approach combines two sources of information: the lexical choices made by people playing different roles on one hand, and the Social Networks describing the interactions between the meeting participants on the other hand. Both sources lead to role recognition results significantly higher than chance when used separately, but the best results are obtained with their combination. Preliminary experiments obtained over a corpus of 138 meeting recordings (over 45 hours of material) show that around 70% of the time is labeled correctly in terms of role.