  We present MarbleNet, an end-to-end neural network for Voice Activity
Detection (VAD). MarbleNet is a deep residual network composed from blocks of
1D time-channel separable convolution, batch-normalization, ReLU and dropout
layers. When compared to a state-of-the-art VAD model, MarbleNet is able to
achieve similar performance with roughly 1/10-th the parameter cost. We further
conduct extensive ablation studies on different training methods and choices of
parameters in order to study the robustness of MarbleNet in real-world VAD
tasks.
