Readings in speech recognition
Fundamentals of speech recognition
Fundamentals of speech recognition
Robust Sensor Fusion: Analysis and Application to Audio Visual Speech Recognition
Machine Learning - Special issue on context sensitivity and concept drift
Speechreading by Man and Machine: Models, Systems, and Applications
Speechreading by Man and Machine: Models, Systems, and Applications
Continuous Audio-Visual Speech Recognition
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Adaptive bimodal sensor fusion for automatic speechreading
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 02
Cross-modal prediction in audio-visual communication
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 04
Audio-visual speech modeling for continuous speech recognition
IEEE Transactions on Multimedia
Designing classifier fusion systems by genetic algorithms
IEEE Transactions on Evolutionary Computation
Vision in HCI: embodiment, multimodality and information capacity
VisHCI '06 Proceedings of the HCSNet workshop on Use of vision in human-computer interaction - Volume 56
Proceedings of the 9th international conference on Multimodal interfaces
Journal of Signal Processing Systems
Information fusion based learning for frugal traffic state sensing
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Audio-Visual Speech Recognition (AVSR) uses vision to enhance speech recognition but also introduces the problem of how to join (or fuse) these two signals together. Mainstream research achieves this using a weighted product of the output of the phoneme classifiers for both modalities. This paper analyses current weighting measures and compares them to several new measures proposed by the authors. Most importantly, when calculating the dispersion of the output there is a shift from analysing the variance to analysing the skewness of the distribution. Experiments in AVSR using neural networks raise questions of the utility of such measures with some intriguing results.