Although topic transition has been studied in dialogue for decades, only a handful of corpora based quantitative studies have been conducted to investigate the nature of topic transitions. Towards this end, this study annotates 215 conversations from the switchboard corpus, perform quantitative analysis and finds that 1) longer conversations consists of more topic transitions, 2) topic transition are usually lead by one participant and 3) we found no pattern in time series progression of topic transition. We also model topic transition with a precision of 91%.

ISO standard 24617-2 for dialogue act annotation, established in 2012, has in the past few years been used both in corpus annotation and in the design of components for spoken and multimodal dialogue systems. This has brought some inaccuracies and undesirbale limitations of the standard to light, which are addressed in a proposed second edition. This second edition allows a more accurate annotation of dependence relations and rhetorical relations in dialogue. Following the ISO 24617-4 principles of semantic annotation, and borrowing ideas from EmotionML, a triple-layered plug-in mechanism is introduced which allows dialogue act descriptions to be enriched with information about their semantic content, about accompanying emotions, and other information, and allows the annotation scheme to be customised by adding application-specific dialogue act types.

Casual conversation has become a focus for artificial dialogue applications. Such talk is ubiquitous and its structure differs from that found in the task-based interactions which have been the focus of dialogue system design for many years. It is unlikely that such conversations can be modelled as an extension of task-based talk. We review theories of casual conversation, report on our studies of the structure of casual dialogue, and outline challenges we see for the development of spoken dialog systems capable of carrying on casual friendly conversation in addition to performing well-defined tasks.

Casual multiparty conversation is an understudied but very common genre of spoken interaction, whose analysis presents a number of challenges in terms of data scarcity and annotation. We describe the annotation process used on the d64 and DANS multimodal corpora of multiparty casual talk, which have been manually segmented, transcribed, annotated for laughter and disfluencies, and aligned using the Penn Aligner. We also describe a visualization tool, STAVE, developed during the annotation process, which allows long stretches of talk or indeed entire conversations to be viewed, aiding preliminary identification of features and patterns worthy of analysis. It is hoped that this tool will be of use to other researchers working in this field.

This paper presents methodologies and tools for language resource (LR) construction. It describes a database of interactive speech collected over a three-month period at the Science Gallery in Dublin, where visitors could take part in a conversation with a robot. The system collected samples of informal, chatty dialogue -- normally difficult to capture under laboratory conditions for human-human dialogue, and particularly so for human-machine interaction. The conversations were based on a script followed by the robot consisting largely of social chat with some task-based elements. The interactions were audio-visually recorded using several cameras together with microphones. As part of the conversation the participants were asked to sign a consent form giving permission to use their data for human-machine interaction research. The multimodal corpus will be made available to interested researchers and the technology developed during the three-month exhibition is being extended for use in education and assisted-living applications.