Joint training of convolutional and non-convolutional neural networks

Abstract

We describe a simple modification of neural networks which consists in extending the commonly used linear layer structure to an arbitrary graph structure. This allows us to combine the benefits of convolutional neural networks with the benefits of regular networks. The joint model has only a small increase in parameter size and training and decoding time are virtually unaffected. We report significant improvements over very strong baselines on two LVCSR tasks and one speech activity detection task. © 2014 IEEE.