State-of-the-Art Speech Recognition Using Multi-Stream Self-Attention with Dilated 1D Convolutions
Journal: 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)
Year: 2019

Self-attention has been a huge success for many downstream tasks in NLP, which led to exploration of applying self-attention to speech problems as well. The efficacy of self-attention in speech applications, however, seems not fully blown yet since it is challenging to handle highly correlated speech frames in the context of self-attention. In this paper we propose a new neural network model architecture, namely multi-stream self-attention, to address the issue thus make the self-attention mecha