By David McNeill (auth.), Steve Renals, Samy Bengio (eds.)
This publication constitutes the completely refereed post-proceedings of the second one overseas Workshop on desktop studying for Multimodal interplay, MLMI 2005, held in Edinburgh, united kingdom in July 2005.
The 38 revised complete papers awarded including 2 invited papers have been rigorously chosen in the course of rounds of reviewing and revision. The papers are geared up in topical sections on multimodal processing, HCI and functions, discourse and discussion, emotion, visible processing, speech and audio processing, and NIST assembly attractiveness evaluation.
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Extra resources for Machine Learning for Multimodal Interaction: Second International Workshop, MLMI 2005, Edinburgh, UK, July 11-13, 2005, Revised Selected Papers
Lunsford, M. Wesson, and L. Carmichael. Toward a theory of organized multmodal integration patterns during human-computer interaction. In Proc. C. ACM Press. 15. S. Oviatt, R. Lunsford, and R. Coulston. Individual diﬀerences in multimodal integration patterns: What are they and why do they exist? In Prof. of CHI’05, pages 241–249. ACM Press. 16. D. Heckerman. A tutorial on learning with Bayesian networks. In Learning in Graphical Modals. MIT Press, 1999. 17. K. Murphy. The Bayes net toolbox for matlab.
Next signal, sequential/simultaneous, etc). Table 3. Prediction result of user modeling study # of training Unimodal Multimodal SIM SEQ samples 5 7,10 2,3,4,8 1,2,3,4,5,6,8, 9 7,10 10 7,10 2,3,4,6,8 1,2,3,4,5,6,8 7 ,9, 10 15 7,9,10 1,2,3,4,5,6, 8 1,2,3,4,5,6,8,9 7, 10 Table 3 shows the prediction accuracy of each subject’s dominant pattern following just 5, 10, or 15 example commands. Based on 5 commands, we can correctly classify 6 subjects with respect to their dominant uni/multimodal pattern.
A TASCAM MX-2424 records the sound tracks from both the wireless and wired microphones. There are some signiﬁcant diﬀerences between our video recording setup and those used by previous eﬀorts. For example, in the NIST and MM4 collections, because stereo camera views are not used to record each participant, only 2D tracking results can be obtained. For the VACE meeting corpus, each participant is recorded with a stereo calibrated camera pair. Ten video cameras are placed facing diﬀerent participants seated around the table as shown in Figure 1.
Machine Learning for Multimodal Interaction: Second International Workshop, MLMI 2005, Edinburgh, UK, July 11-13, 2005, Revised Selected Papers by David McNeill (auth.), Steve Renals, Samy Bengio (eds.)