  This paper presents a new hybrid architecture for voice activity detection
(VAD) incorporating both convolutional neural network (CNN) and bidirectional
long short-term memory (BiLSTM) layers trained in an end-to-end manner. In
addition, we focus specifically on optimising the computational efficiency of
our architecture in order to deliver robust performance in difficult
in-the-wild noise conditions in a severely under-resourced setting. Nested
k-fold cross-validation was used to explore the hyperparameter space, and the
trade-off between optimal parameters and model size is discussed. The
performance effect of a BiLSTM layer compared to a unidirectional LSTM layer
was also considered. We compare our systems with three established baselines on
the AVA-Speech dataset. We find that significantly smaller models with near
optimal parameters perform on par with larger models trained with optimal
parameters. BiLSTM layers were shown to improve accuracy over unidirectional
layers by $\approx$2% absolute on average. With an area under the curve (AUC)
of 0.951, our system outperforms all baselines, including a much larger ResNet
system, particularly in difficult noise conditions.
