  We present a rapid design methodology that combines automated hyper-parameter
tuning with semi-supervised training to build highly accurate and robust models
for voice commands classification. Proposed approach allows quick evaluation of
network architectures to fit performance and power constraints of available
hardware, while ensuring good hyper-parameter choices for each network in
real-world scenarios. Leveraging the vast amount of unlabeled data with a
student/teacher based semi-supervised method, classification accuracy is
improved from 84% to 94% in the validation set. For model optimization, we
explore the hyper-parameter space through population based training and obtain
an optimized model in the same time frame as it takes to train a single model.
