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Visual Voice Activity Detection in the Wild

Research output: Contribution to journalArticleScientificpeer-review


Original languageEnglish
Pages (from-to)967-977
Number of pages11
JournalIEEE Transactions on Multimedia
Issue number6
Publication statusPublished - 1 Jun 2016
Publication typeA1 Journal article-refereed


The visual voice activity detection (V-VAD) problem in unconstrained environments is investigated in this paper. A novel method for V-VAD in the wild, exploiting local shape and motion information appearing at spatiotemporal locations of interest for facial video segment description and the bag of words model for facial video segment representation, is proposed. Facial video segment classification is subsequently performed using the state-of-The-Art classification algorithms. Experimental results on one publicly available V-VAD dataset denote the effectiveness of the proposed method, since it achieves better generalization performance in unseen users, when compared to the recently proposed state-of-The-Art methods. Additional results on a new unconstrained dataset provide evidence that the proposed method can be effective even in such cases in which any other existing method fails.


  • Action Recognition, Bag of Words model, Voice Activity Detection in the wild

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