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We present a rapid design methodology that combines automated hyper-parametertuning with semi-supervised training to build highly accurate and robust modelsfor voice commands classification. Proposed approach allows quick evaluation ofnetwork architectures to fit performance and power constraints of availablehardware, while ensuring good hyper-parameter choices for each network inreal-world scenarios. Leveraging the vast amount of unlabeled data with astudent/teacher based semi-supervised method, classification accuracy isimproved from 84% to 94% in the validation set. For model optimization, weexplore the hyper-parameter space through population based training and obtainan optimized model in the same time frame as it takes to train a single model.

Semi-supervised and Population Based Training for Voice Commands Recognition