Streaming End-to-End ASR based on Blockwise Non-Autoregressive Models

Understand

Non-autoregressive (NAR) modeling has gained more and more attention in speech processing.

• With recent state-of-the-art attention-based automatic speech recognition (ASR) structure, NAR can realize promising real-time factor (RTF) improvement with only small degradation of accuracy compared to the autoregressive (AR) models.
• However, the recognition inference needs to wait for the completion of a full speech utterance, which limits their applications on low latency scenarios.
• To address this issue, we propose a novel end-to-end streaming NAR speech recognition system by combining blockwise-attention and connectionist temporal classification with mask-predict (Mask-CTC) NAR.