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This paper presents an unsupervised segment-based method for robust voiceactivity detection (rVAD). The method consists of two passes of denoisingfollowed by a voice activity detection (VAD) stage. In the first pass,high-energy segments in a speech signal are detected by using a posteriorisignal-to-noise ratio (SNR) weighted energy difference and if no pitch isdetected within a segment, the segment is considered as a high-energy noisesegment and set to zero. In the second pass, the speech signal is denoised by aspeech enhancement method, for which several methods are explored. Next,neighbouring frames with pitch are grouped together to form pitch segments, andbased on speech statistics, the pitch segments are further extended from bothends in order to include both voiced and unvoiced sounds and likely non-speechparts as well. In the end, a posteriori SNR weighted energy difference isapplied to the extended pitch segments of the denoised speech signal fordetecting voice activity. We evaluate the VAD performance of the proposedmethod using two databases, RATS and Aurora-2, which contain a large variety ofnoise conditions. The rVAD method is further evaluated, in terms of speakerverification performance, on the RedDots 2016 challenge database and itsnoise-corrupted versions. Experiment results show that rVAD is comparedfavourably with a number of existing methods. In addition, we present amodified version of rVAD where computationally intensive pitch extraction isreplaced by computationally efficient spectral flatness calculation. Themodified version significantly reduces the computational complexity at the costof moderately inferior VAD performance, which is an advantage when processing alarge amount of data and running on low resource devices. The source code ofrVAD is made publicly available.

rVAD: An Unsupervised Segment-Based Robust Voice Activity Detection Method