Novel voice activity detection based on vector quantization

Meysam Asgari, Abolghasem Sayadian, Farhad Tehranipour, Ali Mostafavi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

In this paper we develop a voice activity detection algorithm based on spectrum estimation of speech and non-speech segments using Vector Quantization method. In this method, we try to classify entry speech signal to speech and non-speech classes. Commonly, the performance of the voice activity detection (VAD) algorithms in non-stationary background noise is not so satisfying under low SNR, so we try to concentrate our study on this issue. The model of a non-speech is a codebook generated from noise and model of speech is several codebook generated from speech contaminated by noise in some different SNR. The labeling is performed by evaluating the distortions between the entry signal samples and the designed models. Our simulation results based on the Persian speech database show that the VQ based VAD is high performance in low SNR conditions (SNR < 5 dB).

Original languageEnglish (US)
Title of host publication11th International Conference on Computer Modelling and Simulation, UKSim 2009
Pages255-257
Number of pages3
DOIs
StatePublished - 2009
Externally publishedYes
Event11th International Conference on Computer Modelling and Simulation, UKSim 2009 - Cambridge, United Kingdom
Duration: Mar 25 2009Mar 27 2009

Publication series

Name11th International Conference on Computer Modelling and Simulation, UKSim 2009

Conference

Conference11th International Conference on Computer Modelling and Simulation, UKSim 2009
CountryUnited Kingdom
CityCambridge
Period3/25/093/27/09

Keywords

  • Vector quantization
  • Voice activity detection

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Modeling and Simulation

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