Non-autoregressive (NAR) modeling has gained significant interest in speech
processing since these models achieve dramatically lower inference time than
autoregressive (AR) models while also achieving good transcription accuracy.
Since NAR automatic speech recognition (ASR) models must wait for the
completion of the entire utterance before processing, some works explore
streaming NAR models based on blockwise attention for low-latency applications.
However, streaming NAR models significantly lag in accuracy compared to
streaming AR and non-streaming NAR models. To address this, we propose a
streaming "semi-autoregressive" ASR model that incorporates the labels emitted
in previous blocks as additional context using a Language Model (LM)
subnetwork. We also introduce a novel greedy decoding algorithm that addresses
insertion and deletion errors near block boundaries while not significantly
increasing the inference time. Experiments show that our method outperforms the
existing streaming NAR model by 19% relative on Tedlium2, 16%/8% on
Librispeech-100 clean/other test sets, and 19%/8% on the Switchboard(SWB) /
Callhome(CH) test sets. It also reduced the accuracy gap with streaming AR and
non-streaming NAR models while achieving 2.5x lower latency. We also
demonstrate that our approach can effectively utilize external text data to
pre-train the LM subnetwork to further improve streaming ASR accuracy.

提出了一种流式的 “半自回归” ASR 模型，通过在上一个片段中引入先前块中发出的标签作为额外上下文，使用语言模型（LM）子网络，以及引入了一种新的贪婪解码算法来解决块边界附近的插入和删除错误，提高了语音处理的准确性和响应速度。