  We propose a single neural network architecture for two tasks: on-line
keyword spotting and voice activity detection. We develop novel inference
algorithms for an end-to-end Recurrent Neural Network trained with the
Connectionist Temporal Classification loss function which allow our model to
achieve high accuracy on both keyword spotting and voice activity detection
without retraining. In contrast to prior voice activity detection models, our
architecture does not require aligned training data and uses the same
parameters as the keyword spotting model. This allows us to deploy a high
quality voice activity detector with no additional memory or maintenance
requirements.
