Entrainment is a phenomenon that occurs across several modalities and at different linguistic levels in conversation. Previous work has shown that its effects may be modulated by conversation extrinsic factors, such as the relation between the interlocutors or the speakers’ traits. The current study investigates the role of conversation type on laughter entrainment. Employing dyadic interaction materials in German, containing two conversation types (free dialogues and task-based interactions), we analyzed three measures of entrainment previously proposed in the literature. The results show that the entrainment effects depend on the type of conversation, with two of the investigated measures being affected by this factor. These findings represent further evidence towards the role of situational aspects as a mediating factor in conversation.

In order to explore intuitive verbal and non-verbal interfaces in smart environments we recorded user interactions with an intelligent apartment. Besides offering various interactive capabilities itself, the apartment is also inhabited by a social robot that is available as a humanoid interface. This paper presents a multi-modal corpus that contains goal-directed actions of naive users in attempts to solve a number of predefined tasks. Alongside audio and video recordings, our data-set consists of large amount of temporally aligned sensory data and system behavior provided by the environment and its interactive components. Non-verbal system responses such as changes in light or display contents, as well as robot and apartment utterances and gestures serve as a rich basis for later in-depth analysis. Manual annotations provide further information about meta data like the current course of study and user behavior including the incorporated modality, all literal utterances, language features, emotional expressions, foci of attention, and addressees.

The Active Listening Corpus (ALICO) is a multimodal database of spontaneous dyadic conversations with diverse speech and gestural annotations of both dialogue partners. The annotations consist of short feedback expression transcription with corresponding communicative function interpretation as well as segmentation of interpausal units, words, rhythmic prominence intervals and vowel-to-vowel intervals. Additionally, ALICO contains head gesture annotation of both interlocutors. The corpus contributes to research on spontaneous human–human interaction, on functional relations between modalities, and timing variability in dialogue. It also provides data that differentiates between distracted and attentive listeners. We describe the main characteristics of the corpus and present the most important results obtained from analyses in recent years.