Bayesian network models provide an attractive framework for multitnodal sensor fusion. They combine an intuitive graphical representation with efficient algorithms for inference und learning. However, the unsupervised nature of standard parameter (earning algorithms for Bayesian networks can lead to poor performance in classification tasks. We have developed a supervised learning framework for Bayesian networks, which is based on the Adaboost algorithm of Schapire and Freund. Our framework covers static and dynamic Bayesian networks with both discrete and continuous states. We have tested our framework in the context of a novel multimodal HCI application: a speech-based command and control interface for a Smart Kiosk. We provide experimental evidence for the utility of our boosted learning approach.
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
- Bayesian networks
- Discriminative learning
- Muitimodal integration
- Speaker detection