  Speech Activity Detection (SAD), locating speech segments within an audio
recording, is a main part of most speech technology applications. Robust SAD is
usually more difficult in noisy conditions with varying signal-to-noise ratios
(SNR). The Fearless Steps challenge has recently provided such data from the
NASA Apollo-11 mission for different speech processing tasks including SAD.
Most audio recordings are degraded by different kinds and levels of noise
varying within and between channels. This paper describes the EML online
algorithm for the most recent phase of this challenge. The proposed algorithm
can be trained both in a supervised and unsupervised manner and assigns speech
and non-speech labels at runtime approximately every 0.1 sec. The experimental
results show a competitive accuracy on both development and evaluation datasets
with a real-time factor of about 0.002 using a single CPU machine.
