Probabilistic independence networks for hidden Markov probability models
Neural Computation
The Hierarchical Hidden Markov Model: Analysis and Applications
Machine Learning
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Four Paradigms for Indexing Video Conferences
IEEE MultiMedia
Speech recognition with dynamic bayesian networks
Speech recognition with dynamic bayesian networks
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Automatic Analysis of Multimodal Group Actions in Meetings
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
Multimodal integration for meeting group action segmentation and recognition
MLMI'05 Proceedings of the Second international conference on Machine Learning for Multimodal Interaction
| Hi-index | 0.00 |
This paper investigates the automatic analysis and segmentation of meetings. A meeting is analysed in terms of individual behaviours and group interactions, in order to decompose each meeting in a sequence of relevant phases, named meeting actions. Three feature families are extracted from multimodal recordings: prosody from individual lapel microphone signals, speaker activity from microphone array data and lexical features from textual transcripts. A statistical approach is then used to relate low-level features with a set of abstract categories. In order to provide a flexible and powerful framework, we have employed a dynamic Bayesian network based model, characterized by multiple stream processing and flexible state duration modelling. Experimental results demonstrate the strength of this system, providing a meeting action error rate of 9%.