HMM and BPNN based speech recognition system for home service robot

Chih Yin Liu, Tzu Hsin Hung, Kai Chung Cheng, Tzuu Hseng S. Li

研究成果: Conference contribution

5 引文 斯高帕斯(Scopus)

摘要

This paper proposes a two-stage speech recognition system based on hidden Markov model (HMM) and back-propagation neural network (BPNN) for home service robot. Since a home service robot would interact with different users, a speaker independent and robust system should be developed. The recognition system we proposed contains two learning stages to build the models of words. In the first stage, the Gaussian mixture model (GMM) likelihood probabilities are calculated by HMM. And then, the probabilities are treated as the input units of neural network in the second stage. The home service robot, May-1 is designed and implemented for realizing the speech recognition system. The experimental results show that the robot can successfully complete follow-me, recognition of names, and recognition of rooms tasks in the RoboCup@ Home league competition.

原文English
主出版物標題2013 International Conference on Advanced Robotics and Intelligent Systems, ARIS 2013 - Conference Proceedings
頁面38-43
頁數6
DOIs
出版狀態Published - 2013 九月 9
事件2013 International Conference on Advanced Robotics and Intelligent Systems, ARIS 2013 - Tainan, Taiwan
持續時間: 2013 五月 32013 六月 2

出版系列

名字2013 International Conference on Advanced Robotics and Intelligent Systems, ARIS 2013 - Conference Proceedings

Other

Other2013 International Conference on Advanced Robotics and Intelligent Systems, ARIS 2013
國家Taiwan
城市Tainan
期間13-05-0313-06-02

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Control and Systems Engineering

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  • 引用此

    Liu, C. Y., Hung, T. H., Cheng, K. C., & Li, T. H. S. (2013). HMM and BPNN based speech recognition system for home service robot. 於 2013 International Conference on Advanced Robotics and Intelligent Systems, ARIS 2013 - Conference Proceedings (頁 38-43). [6573531] (2013 International Conference on Advanced Robotics and Intelligent Systems, ARIS 2013 - Conference Proceedings). https://doi.org/10.1109/ARIS.2013.6573531