  In this study, we propose an encoder-decoder structured system with fully
convolutional networks to implement voice activity detection (VAD) directly on
the time-domain waveform. The proposed system processes the input waveform to
identify its segments to be either speech or non-speech. This novel
waveform-based VAD algorithm, with a short-hand notation "WVAD", has two main
particularities. First, as compared to most conventional VAD systems that use
spectral features, raw-waveforms employed in WVAD contain more comprehensive
information and thus are supposed to facilitate more accurate speech/non-speech
predictions. Second, based on the multi-branched architecture, WVAD can be
extended by using an ensemble of encoders, referred to as WEVAD, that
incorporate multiple attribute information in utterances, and thus can yield
better VAD performance for specified acoustic conditions. We evaluated the
presented WVAD and WEVAD for the VAD task in two datasets: First, the
experiments conducted on AURORA2 reveal that WVAD outperforms many
state-of-the-art VAD algorithms. Next, the TMHINT task confirms that through
combining multiple attributes in utterances, WEVAD behaves even better than
WVAD.
