Regularizing DNN acoustic models with Gaussian stochastic neurons

Dropout and DropConnect can be viewed as regularization methods for deep neural network (DNN) training. In DNN acoustic modeling, the huge number of speech samples makes it expensive to sample the neuron mask (Dropout) or the weight mask (DropConnect) repetitively from a high dimensional distribution. In this paper we investigate the effect of Gaussian stochastic neurons on DNN acoustic modeling.

The pre-Gaussian stochastic term can be viewed as a variant of Dropout/DropConnect and the post-Gaussian stochastic term generalizes the idea of data augmentation into hidden layers. Gaussian stochastic neurons can give improvement on large data sets where Dropout tends to be less useful. Under the low resource condition, its performance is comparable with Dropout, but with a lower time complexity during fine-tuning.

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