Voice Activity Detection: Merging Source and Filter-based Information

Thomas Drugman, Yannis Stylianou, Yusuke Kida, Masami Akamine

Research output: Contribution to journalArticlepeer-review

55 Citations (Scopus)


Voice Activity Detection (VAD) refers to the problem of distinguishing speech segments from background noise. Numerous approaches have been proposed for this purpose. Some are based on features derived from the power spectral density, others exploit the periodicity of the signal. The goal of this letter is to investigate the joint use of source and filter-based features. Interestingly, a mutual information-based assessment shows superior discrimination power for the source-related features, especially the proposed ones. The features are further the input of an artificial neural network-based classifier trained on a multi-condition database. Two strategies are proposed to merge source and filter information: feature and decision fusion. Our experiments indicate an absolute reduction of 3% of the equal error rate when using decision fusion. The final proposed system is compared to four state-of-The-Art methods on 150 minutes of data recorded in real environments. Thanks to the robustness of its source-related features, its multi-condition training and its efficient information fusion, the proposed system yields over the best state-of-The-Art VAD a substantial increase of accuracy across all conditions (24% absolute on average).

Original languageEnglish
Article number7307972
Pages (from-to)252-256
Number of pages5
JournalIEEE Signal Processing Letters
Issue number2
Publication statusPublished - 2016 Feb 1
Externally publishedYes


  • Excitation
  • information fusion
  • periodicity
  • voice activity detection

ASJC Scopus subject areas

  • Signal Processing
  • Applied Mathematics
  • Electrical and Electronic Engineering


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