  Voice activity detection (VAD) is an important pre-processing step for speech
technology applications. The task consists of deriving segment boundaries of
audio signals which contain voicing information. In recent years, it has been
shown that voice source and vocal tract system information can be extracted
using zero-frequency filtering (ZFF) without making any explicit model
assumptions about the speech signal. This paper investigates the potential of
zero-frequency filtering for jointly modeling voice source and vocal tract
system information, and proposes two approaches for VAD. The first approach
demarcates voiced regions using a composite signal composed of different
zero-frequency filtered signals. The second approach feeds the composite signal
as input to the rVAD algorithm. These approaches are compared with other
supervised and unsupervised VAD methods in the literature, and are evaluated on
the Aurora-2 database, across a range of SNRs (20 to -5 dB). Our studies show
that the proposed ZFF-based methods perform comparable to state-of-art VAD
methods and are more invariant to added degradation and different channel
characteristics.
