Investigating sparse deep neural networks for speech recognition

We propose an organized sparse deep neural network architecture for automatic speech recognition. The proposed method is inspired by the tonotopic organization in the auditory nerve/cortex. The approach consists of limiting the neurons connections between the hidden layers, in a manner that preserves frequency proximity, resulting in a diffuse integration of the spectral information inside the neural network. This method is put in perspective with related work on sparser neural network architectures for speech recognition (tonotopy, convolutional nets, dropout). The model is trained and tested on the TIMIT database, showing encouraging results compared to the traditional fully connected architecture.