Research on multimodal human-robot interaction based on speech and gesture. (November 2018)

• Record Type: Journal Article
• Authors: Yongda, Deng; Fang, Li; Huang, Xin
• Is Part Of: Computers & electrical engineering. Volume 72(2018)
• Page Start: 443
• Page End: 454
• Publication Date: 2018-11
• Subjects: Human robot interaction -- Fusion of speech and gesture -- Interval Kalman Filter (IKF) -- Maximum entropy classification -- Natural language understanding; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2018.09.014
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: This paper presents a multimodal human-robot interaction based on fusion of speech and gesture. In the interface, a robot control command system is designed, which can transform the speech and gesture of users into commands that the robot can execute. Microsoft speech SDK is used in this system to collect the speech of the operator. Then, a corpus-based algorithm of maximum entropy classification for natural language understanding is employed to generate commands. Leap Motion is employed to capture the gesture of operator in this system. Interval Kalman Filter (IKF) is used to estimate the measured data to reduce the inherent noise of the sensor. The advantage of the proposed method is that the combination of speech and gesture makes the human-robot interaction more convenient and direct. Finally, a series of experiments were carried out to validate our method, and proved that it performed better than the other proposed methods.