International Journal of Advanced Robotic Systems | |
Audio-Visual Tibetan Speech Recognition Based on a Deep Dynamic Bayesian Network for Natural Human Robot Interaction | |
关键词: Audio-visual speech recognition; Deep Dynamic Bayesian Network; unsupervised feature learning; Tibetan speech recognition; | |
DOI : 10.5772/54000 | |
学科分类:自动化工程 | |
来源: InTech | |
【 摘 要 】
Audio-visual speech recognition is a natural and robust approach to improving human-robot interaction in noisy environments. Although multi-stream Dynamic Bayesian Network and coupled HMM are widely used for audio-visual speech recognition, they fail to learn the shared features between modalities and ignore the dependency of features among the frames within each discrete state. In this paper, we propose a Deep Dynamic Bayesian Network (DDBN) to perform unsupervised extraction of spatial-temporal multimodal features from Tibetan audio-visual speech data and build an accurate audio-visual speech recognition model under a no frame-independency assumption. The experiment results on Tibetan speech data from some real-world environments showed the proposed DDBN outperforms the state-of-art methods in word recognition accuracy.
【 授权许可】
CC BY
【 预 览 】
Files | Size | Format | View |
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RO201902187840838ZK.pdf | 1160KB | download |