Comparative evaluation of different methods for voice activity detection

Hongfei Ding, Koichi Yamamoto, Masami Akamine

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)

Abstract

This paper presents a comparative evaluation of different methods for voice activity detection (VAD). A novel feature set is proposed in order to improve VAD performance in diverse noisy environments. Furthermore, three classifiers for VAD are evaluated. The three classifiers are Gaussian Mixture Model (GMM), Support Vector Machine (SVM) and Decision Tree (DT). Experimental results show that the proposed feature set achieves better performance than spectral entropy. In the comparison of the classifiers, DT shows the best performance in terms of frame-based VAD accuracy as well as computational cost.

Original languageEnglish
Pages (from-to)107-110
Number of pages4
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Publication statusPublished - 2008 Dec 1
Externally publishedYes
EventINTERSPEECH 2008 - 9th Annual Conference of the International Speech Communication Association - Brisbane, QLD, Australia
Duration: 2008 Sep 222008 Sep 26

Keywords

  • Decision Tree (DT)
  • Gaussian Mixture Model (GMM)
  • Support Vector Machine (SVM)
  • Voice activity detection (VAD)

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Signal Processing
  • Software
  • Sensory Systems

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