| A conceptual framework for developing adaptive multimodal applications |
| Full text |
Pdf
(723 KB)
|
| Source
|
International Conference on Intelligent User Interfaces
archive
Proceedings of the 11th international conference on Intelligent user interfaces
table of contents
Sydney, Australia
SESSION: Multimedia and multimodality
table of contents
Pages: 132 - 139
Year of Publication: 2006
ISBN:1-59593-287-9
|
|
Authors
|
|
| Sponsors |
|
| Publisher |
|
| Bibliometrics |
Downloads (6 Weeks): 17, Downloads (12 Months): 119, Citation Count: 0
|
|
|
ABSTRACT
This article presents FAME, a model-based Framework for Adaptive Multimodal Environments. FAME proposes an architecture for adaptive multimodal applications, a new way to represent adaptation rules - the behavioral matrix - and a set of guidelines to assist the design process of adaptive multimodal applications. To demonstrate FAME's validity, the development process of an adaptive Digital Talking Book player is summarized.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
 |
1
|
|
 |
2
|
|
| |
3
|
Gaelle Calvary , Joëlle Coutaz , David Thevenin , Quentin Limbourg , Nathalie Souchon , Laurent Bouillon , Murielle Florins , Jean Vanderdonckt, Plasticity of User Interfaces: A Revised Reference Framework, Proceedings of the First International Workshop on Task Models and Diagrams for User Interface Design, p.127-134, July 18-19, 2002
|
 |
4
|
|
 |
5
|
|
 |
6
|
Christian Elting , Stefan Rapp , Gregor Möhler , Michael Strube, Architecture and implementation of multimodal plug and play, Proceedings of the 5th international conference on Multimodal interfaces, November 05-07, 2003, Vancouver, British Columbia, Canada
[doi> 10.1145/958432.958453]
|
 |
7
|
|
| |
8
|
A. Garg, V. PavloviĆ, and J. Rehg. Boosted learning in dynamic bayesian networks for multimodal speaker detection. Proceedings of the IEEE, 91(9):1355--1369, 2003.
|
 |
9
|
|
| |
10
|
M. Harders and G. Székely. Enhancing human-computer interaction in medical segmentation. Proceedings of the IEEE, 91(9):1430--1442, 2003.
|
 |
11
|
|
| |
12
|
|
 |
13
|
|
| |
14
|
S. Oviatt. User-centered modeling and evaluation of multimodal interfaces. Proceedings of the IEEE, 91(9):1457--1468, 2003.
|
 |
15
|
|
| |
16
|
S. Oviatt, T. Darrell, and M. Flickner. Multimodal interfaces that flex, adapt, and persist. Commun. ACM, 47(1), 2004.
|
| |
17
|
S. L. Oviatt, P. R. Cohen, L. Wu, J. Vergo, L. Duncan, B. Suhm, J. Bers, T. Holzman, T. Winograd, J. Landay, J. Larson, and D. Ferro. Designing the user interface for multimodal speech and gesture applications: State-of-the-art systems and research directions. Human Computer Interaction, 15(4):263--322, 2000.
|
| |
18
|
|
| |
19
|
R. Sharma, M. Yeasin, N. Krahnstoever, I. Rauschert, G. Cai, I. Brewer, A. M. Maceachren, and K. Sengupta. Speech-gesture driven multimodal interfaces for crisis management. Proceedings of the IEEE, 91(9):1327--1354, 2003.
|
| |
20
|
C. Stephanidis and A. Savidis. Universal access in the information society: Methods, tools and interaction technologies. Universal Access in the Information Society, 1(1):40--55, 2001.
|
| |
21
|
C. Wickens and J. Hollands. Engineering Psychology and Human Performance. Prentice Hall, 1999.
|
|