Improving Very Deep Time-Delay Neural Network With Vertical-Attention For Effectively Training CTC-Based ASR Systems

Abstract

The very deep neural network has recently been proposed for speech recognition and achieves significant performance. It has excellent potential for integration with end-to-end (E2E) training. Connectionist temporal classification (CTC) has shown great potential in E2E acoustic modeling. In this study, we investigate deep architectures and techniques which are suitable for CTC-based acoustic modeling. We propose a very deep residual time-delay CTC neural network (VResTD-CTC). How to select a suitable deep architecture optimized with the CTC objective function is crucial for obtaining the state of the art performance. Excellent performances can be obtained by selecting deep architecture for non-E2E ASR systems modeling with tied-triphone states. However, these optimized structures do not guarantee to achieve better or comparable performances on E2E (e.g., CTC-based) systems modeling with dynamic acoustic units. For solving this problem and further leveraging the system performance, we introduce the vertical-attention mechanism to reweight the residual blocks at each time step. Speech recognition experiments show our proposed model significantly outperforms the DNN and LSTM-based (both bidirectional and unidirectional) CTC baseline models.