The AMI Meeting Corpus is a multi-modal data set consisting of 100 hours of meeting recordings. It is being created in the context of a project that is developing meeting browsing technology and will eventually be released publicly. Some of the meetings it contains are naturally occurring, and some are elicited, particularly using a scenario in which the participants play different roles in a design team, taking a design project from kick-off to completion over the course of a day. The corpus is being recorded using a wide range of devices including close-talking and far-field microphones, individual and room-view video cameras, projection, a whiteboard, and individual pens, all of which produce output signals that are synchronized with each other. It is also being hand-annotated for many different phenomena, including orthographic transcription, discourse properties such as named entities and dialogue acts, summaries, emotions, and some head and hand gestures. We describe the data set, including the rationale behind using elicited material, and explain how the material is being recorded, transcribed and annotated.

Abstract.The AMI Meeting Corpus contains 100 hours of meetings captured using many synchronized recording devices, and is designed to support work in speech and video processing, language engineering, corpus linguistics, and organizational psychology. It has been transcribed orthographically, with annotated subsets for everything from named entities, dialogue acts, and summaries to simple gaze and head movement. In this written version of an LREC conference keynote address, I describe the data and how it was created. If this is "killer" data, that presupposes a platform that it will "sell"; in this case, that is the NITE XML Toolkit, which allows a distributed set of users to create, store, browse, and search annotations for the same base data that are both time-aligned against signal and related to each other structurally.

Current models draw a broad distinction between communication as dialogue and communication as monologue. The two kinds of models have different implications for who influences whom in a group discussion. If the discussion is like interactive dialogue, group members should be influenced most by those with whom they interact in the discussion; if it is like serial monologue, they should be influenced most by the dominant speaker. The experiments reported here show that in small, 5-person groups, the communication is like dialogue and members are influenced most by those with whom they interact in the discussion. However, in large, 10-person groups, the communication is like monologue and members are influenced most by the dominant speaker. The difference in mode of communication is explained in terms of how speakers in the two sizes of groups design their utterances for different audiences.

In order to build robust automatic abstracting systems, there is a need for better training resources than are currently available. In this paper, we introduce an annotation scheme for scientific articles which can be used to build such a resource in a consistent way. The seven categories of the scheme are based on rhetorical moves of argumentation. Our experimental results show that the scheme is stable, reproducible and intuitive to use.

This paper describes a recently completed common resource for the study of spoken discourse, the NXT-format Switchboard Corpus. Switchboard is a long-standing corpus of telephone conversations (Godfrey et al. in SWITCH-BOARD: Telephone speech corpus for research and development. In Proceedings of ICASSP-92, pp. 517-520, 1992). We have brought together transcriptions with existing annotations for syntax, disfluency, speech acts, animacy, information status, coreference, and prosody; along with substantial new annotations of focus/contrast, more prosody, syllables and phones. The combined corpus uses the format of the NITE XML Toolkit, which allows these annotations to be browsed and searched as a coherent set (Carletta et al. in Lang Resour Eval J 39(4):313-334, 2005). The resulting corpus is a rich resource for the investigation of the linguistic features of dialogue and how they interact. As well as describing the corpus itself, we discuss our approach to overcoming issues involved in such a data integration project, relevant to both users of the corpus and others in the language resource community undertaking similar projects.