Advances in Very Deep Convolutional Neural Networks for LVCSR

Very deep CNNs with small 3 × 3 kernels have recently been shown to achieve very strong performance as acoustic models in hybrid NN-HMM speech recognition systems. In this paper, we demonstrate that the accuracy gains of these deep CNNs are retained both on larger scale data, and after sequence training. We show this by carrying out sequence training on both the 300h switchboard-1 and the 2000h switchboard dataset. Furthermore, we investigate how pooling and padding in time influences performance, both in terms of word error rate and computational cost. We argue that designing CNNs without timepadding and without timepooling, though slightly suboptimal for accuracy, has two significant consequences. Firstly, the proposed design allows for efficient evaluation at sequence training and test (deployment) time. Secondly, this design principle allows for batch normalization to be adopted to CNNs on sequence data. Our very deep CNN model sequence trained on the 2000h switchboard dataset obtains 9.4 word error rate on the Hub5 test-set, matching with a single model the performance of 2015 IBM system combination, which was the previous best published result.