  State-of-the-art deep-learning-based voice activity detectors (VADs) are
often trained with anechoic data. However, real acoustic environments are
generally reverberant, which causes the performance to significantly
deteriorate. To mitigate this mismatch between training data and real data, we
simulate an augmented training set that contains nearly five million
utterances. This extension comprises of anechoic utterances and their
reverberant modifications, generated by convolutions of the anechoic utterances
with a variety of room impulse responses (RIRs). We consider five different
models to generate RIRs, and five different VADs that are trained with the
augmented training set. We test all trained systems in three different real
reverberant environments. Experimental results show $20\%$ increase on average
in accuracy, precision and recall for all detectors and response models,
compared to anechoic training. Furthermore, one of the RIR models consistently
yields better performance than the other models, for all the tested VADs.
Additionally, one of the VADs consistently outperformed the other VADs in all
experiments.
