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Dynamic Bayesian networks for audio-visual speech …
https://www.cs.ubc.ca/~murphyk/Papers/avsr_journal.pdf
Dynamic Bayesian Networks for Audio-Visual Speech Recognition 3 A 1V1 A 2 3 4 5 A 1V 2 A 2V 2 A 3V 2 A 4V 2 A 5V 2 A 1V 3 A 2V 3 A 3V 3 A 4V 3 A 5V 3 A 1V 4 A 2V 4 A ...
Dynamic Bayesian Networks for Audio-Visual Speech ...
https://asp-eurasipjournals.springeropen.com/articles/10.1155/S1110865702206083
Dynamic Bayesian Networks for Audio-Visual Speech Recognition. Ara V. Nefian 1, Luhong Liang 2, Xiaobo Pi 2, Xiaoxing Liu 2 & Kevin Murphy 3 EURASIP Journal on Advances in Signal Processing volume 2002, Article number: 783042 (2002) Cite this article
(PDF) Dynamic Bayesian Networks for Audio-Visual …
https://www.researchgate.net/publication/26532592_Dynamic_Bayesian_Networks_for_Audio-Visual_Speech_Recognition
K e ywords and phrases: audio-visual speech recognition, hidden Markov mo dels, coupled hidden Markov models, factorial hid- den Markov models, dynamic Bayesian networks. 1.
Dynamic Bayesian Networks for Audio-Visual Speaker Recognition
https://link.springer.com/chapter/10.1007/11608288_72
Abstract. Audio-Visual speaker recognition promises higher performance than any single modal biometric systems. This paper further improves the novel approach based on Dynamic Bayesian Networks (DBNs) to bimodal speaker recognition. In the present paper, we investigate five different topologies of feature-level fusion framework using DBNs.
DynamicBayesianNetworksforAudio-Visual SpeechRecognition
https://link.springer.com/content/pdf/10.1155%2FS1110865702206083.pdf
Dynamic Bayesian Networks for Audio-Visual Speech Recognition 1275 Video sequence Audio sequence Video feature extraction Upsampling Acoustic feature extraction Audio-visual model Training/ Recognition Figure 1: The audio-visual speech recognition system. A1V1 A2V2 A3V3 A4V4 A5V5 Figure 2: The state transition diagram of a left-to-right HMM.
Dynamic Bayesian Networks for Audio-Visual Speech ...
https://ui.adsabs.harvard.edu/abs/2002EJASP2002...38N/abstract
The use of visual features in audio-visual speech recognition (AVSR) is justified by both the speech generation mechanism, which is essentially bimodal in audio and visual representation, and by the need for features that are invariant to acoustic noise perturbation. As a result, current AVSR systems demonstrate significant accuracy improvements in environments …
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