Gerasimos Potamianos, Chalapathy Neti, et al.
Proceedings of the IEEE
In this paper, we propose a new fast and flexible algorithm based on the maximum entropy (MAXENT) criterion to estimate stream weights in a state-synchronous multi-stream HMM. The technique is compared to the minimum classification error (MCE) criterion and to a brute-force, grid-search optimization of the WER on both a small and a large vocabulary audio-visual continuous speech recognition task. When estimating global stream weights, the MAXENT approach gives comparable results to the grid-search and the MCE. Estimation of state dependent weights is also considered: We observe significant improvements in both the MAXENT and MCE criteria, which, however, do not result in significant WER gains.
Gerasimos Potamianos, Chalapathy Neti, et al.
Proceedings of the IEEE
Patrick Lucey, Gerasimos Potamianos, et al.
AVSP 2008
Etienne Marcheret, Gerasimos Potamianos, et al.
INTERSPEECH 2015
Vaibhava Goel, Scott Axelrod, et al.
INTERSPEECH - Eurospeech 2003