Fine context, low-rank, softplus deep neural networks for mobile speech recognition

We investigate the use of large state inventories and the softplus nonlinearity for on-device neural network based mobile speech recognition. Large state inventories are achieved by less aggressive context-dependent state tying, and made possible by using a bottleneck layer to contain the number of parameters. We investigate alternative approaches to the bottleneck layer, demonstrate the superiority of the softplus non-linearity and investigate alternatives for the final stages of the training algorithm. Overall we reduce the word error rate of the system by 9% relative. The techniques are also shown to work well for large acoustic models for cloud-based speech recognition.