Formant-based robust voice activity detection

In Chul Yoo, Hyeontaek Lim, Dongsuk Yook

Research output: Contribution to journalArticlepeer-review

46 Citations (Scopus)


Voice activity detection (VAD) can be used to distinguish human speech from other sounds, and various applications can benefit from VAD-including speech coding and speech recognition. To accurately detect voice activity, the algorithm must take into account the characteristic features of human speech and/or background noise. In many real-life applications, noise frequently occurs in an unexpected manner, and in such situations, it is difficult to determine the characteristics of noise with sufficient accuracy. As a result, robust VAD algorithms that depend less on making correct noise estimates are desirable for real-life applications. Formants are the major spectral peaks of the human voice, and these are highly useful to distinguish vowel sounds. The characteristics of the spectral peaks are such that, these peaks are likely to survive in a signal after severe corruption by noise, and so formants are attractive features for voice activity detection under low signal-to-noise ratio (SNR) conditions. However, it is difficult to accurately extract formants from noisy signals when background noise introduces unrelated spectral peaks. Therefore, this paper proposes a simple formant-based VAD algorithm to overcome the problem of detecting formants under conditions with severe noise. The proposed method achieves a much faster processing time and outperforms standard VAD algorithms under various noise conditions. The proposed method is robust against various types of noise and produces a light computational load, so it is suitable for use in various applications.

Original languageEnglish
Article number7239555
Pages (from-to)2238-2245
Number of pages8
JournalIEEE Transactions on Audio, Speech and Language Processing
Issue number12
Publication statusPublished - 2015 Dec 1

Bibliographical note

Publisher Copyright:
Copyright © 2015 IEEE.


  • Formants
  • Spectral peaks
  • Voice activity detection (VAD)

ASJC Scopus subject areas

  • Acoustics and Ultrasonics
  • Electrical and Electronic Engineering


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