The NIST Smart Space and Meeting Room projects: signals, acquisition annotation, and metrics

Stanford, Vincent M.; Garofolo, John S.; Galibert, Olivier; Michel, Martial; Laprun, Christophe

doi:10.1109/icassp.2003.1202748

Cited by 32 publications

(17 citation statements)

References 1 publication

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“…The meeting corpus from the Multimodal Meeting Manager (M4) European Project [91]. The meeting corpus from the US National Institute of Standards and Technology (NIST) [119,51]. The meeting corpus from the Augmented-Multi-Party Interaction (AMI) European Project [22,23].…”

Section: Research Infrastructure Resourcesmentioning

confidence: 99%

Automatic nonverbal analysis of social interaction in small groups: A review

Gática-Pérez

2009

Image and Vision Computing

280

183

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Section: Research Infrastructure Resourcesmentioning

confidence: 99%

Automatic nonverbal analysis of social interaction in small groups: A review

Gática-Pérez

2009

Image and Vision Computing

280

183

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“…In both series, the US National Institute for Standard Technology (NIST) has played a pivotal role in gathering normalized data that was considered by participants to be representative of the addressed research questions. Along with external data from the AMI and CHIL consortia, NIST has also produced original data in its own instrumented meeting rooms, starting from the Smart Spaces Laboratory [Stanford et al, 2003].…”

Section: Joint Evaluation and Dissemination Activitiesmentioning

confidence: 99%

Multimodal signal processing for meetings: an introduction

Popescu-Belis

Carletta

2012

Multimodal Signal Processing

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This book is an introduction to multimodal signal processing. In it, we use the goal of building applications that can understand meetings as a way to focus and motivate the processing we describe. Multimodal signal processing takes the outputs of capture devices running at the same time -primarily cameras and microphones, but also electronic whiteboards and pens -and automatically analyses them to make sense of what is happening in the space being recorded. For instance, these analyses might indicate who spoke, what was said, whether there was an active discussion, and who was dominant in it. These analyses require the capture of multimodal data using a range of signals, followed by a low-level automatic annotation of them, gradually layering up annotation until information that relates to user requirements is extracted.Multimodal signal processing can be done in real time, that is, fast enough to build applications that influence the group while they are together, or offline -not always but often at higher quality -for later review of what went on. It can also be done for groups that are all together in one space, typically an instrumented meeting room, or for groups that are in different spaces but use technology such as video-conferencing to communicate. The book thus introduces automatic approaches to capturing, processing and ultimately understanding human interaction in meetings, and describes the state-of-the-art for all technologies involved.Multimodal signal processing raises the possibility of a wide range of applications that help groups improve their interactions and hence their effectiveness between or during meetings. However, developing applications has required improvements in the technological state-of-theart in many arenas.The first comprises core technologies like audio and visual processing and recognition that tell us basic facts such as who was present and what words were said. On top of this information comes processing that begins to make sense of a meeting in human terms. Part of this is simply combining different sources of information into a record of who said what, when, and to whom, but it is often also useful, for instance, to apply models of group dynamics from the behavioral and social sciences in order to reveal how a group interacts, or to abstract and summarize the meeting content overall. Finding ways to integrate the varying analyses required for a particular meeting support application has been a major new challenge.Finally, moving from components that model and analyze multimodal human-to-human communication scenes to real-world applications has required careful user requirements capture,

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“…We propose to use the NIST SmartFlow system that allows the transportation of large amounts of data from sensors to recognition algorithms running on distributed, networked nodes [2, 3]. The working installations of SmartFlow is reportedly able to support hundreds of sensors [42]. In the present version of our system, the integration was not completed, as some modules are implemented with MATLAB, and data exchange of modules was simulated.…”

Section: The Middlewarementioning

confidence: 99%

Multimodal identification and localization of users in a smart environment

Salah

Morros

Luque

et al. 2008

J Multimodal User Interfaces

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Detecting the location and identity of users is a first step in creating contextaware applications for technologically-endowed environments. We propose a system that makes use of motion detection, person tracking, face identification, feature-based identification, audio-based localization, and audio-based identification modules, fusing information with particle filters to obtain robust localization and identification. The data streams are processed with the help of the generic client-server middleware SmartFlow, resulting in a flexible architecture that runs across different platforms.

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The NIST Smart Space and Meeting Room projects: signals, acquisition annotation, and metrics

Cited by 32 publications

References 1 publication

Automatic nonverbal analysis of social interaction in small groups: A review

Automatic nonverbal analysis of social interaction in small groups: A review

Multimodal signal processing for meetings: an introduction

Multimodal identification and localization of users in a smart environment

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