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Talking vs Non-Talking: A Vision Based Approach to Detect Human Speaking Mode

机译:说话与不说话:基于视觉的人类说话模式检测方法

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Human talking mode detection is an important issue in human-computer interaction. In this work, we propose a method for detecting human talking and non talking mode detection based on supervised machine learning approach. Visual lip information of human is considered as an important clue. Our goal is to develop a method for human talking and non talking mode detection in real time using supervised classification algorithm. We tested our experiment with a single speaker task and compared the results with the previous method. The results show that our approach can obtain a 98.00% accuracy and a fast executed time.
机译:语音模式检测是人机交互中的重要问题。在这项工作中,我们提出了一种基于监督机器学习方法的人类说话和非说话模式检测方法。人的视觉嘴唇信息被认为是重要的线索。我们的目标是开发一种使用监督分类算法实时检测人类说话和非说话模式的方法。我们使用单个发言人任务测试了我们的实验,并将结果与​​以前的方法进行了比较。结果表明,我们的方法可以获得98.00%的准确度和快速的执行时间。

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