Journal of Electrical Engineering and Technology

The Korean Institute of Electrical Engineers (대한전기학회)
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Applying the Bi-level HMM for Robust Voice-activity Detection

  • Hwang, Yongwon (Dept. of Electrical and Electronic Engineering, Yonsei University) ;
  • Jeong, Mun-Ho (School of Robotics, Kwangwoon University) ;
  • Oh, Sang-Rok (Center for Robotics Research, Korea Institute of Science and Technology) ;
  • Kim, Il-Hwan (Dept. of Electronic and Communication, Kwangwoon National University)
  • Received : 2016.01.16
  • Accepted : 2016.05.29
  • Published : 2017.01.02
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Abstract

This paper presents a voice-activity detection (VAD) method for sound sequences with various SNRs. For real-time VAD applications, it is inadequate to employ a post-processing for the removal of burst clippings from the VAD output decision. To tackle this problem, building on the bi-level hidden Markov model, for which a state layer is inserted into a typical hidden Markov model (HMM), we formulated a robust method for VAD not requiring any additional post-processing. In the method, a forward-inference-ratio test was devised to detect the speech endpoints and Mel-frequency cepstral coefficients (MFCC) were used as the features. Our experiment results show that, regarding different SNRs, the performance of the proposed approach is more outstanding than those of the conventional methods.

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References

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