In this paper, we present ZeroPrompt (Figure 1-(a)) and the corresponding
Prompt-and-Refine strategy (Figure 3), two simple but effective
\textbf{training-free} methods to decrease the Token Display Time (TDT) of
streaming ASR models \textbf{without any accuracy loss}. The core idea of
ZeroPrompt is to append zeroed content to each chunk during inference, which
acts like a prompt to encourage the model to predict future tokens even before
they were spoken. We argue that streaming acoustic encoders naturally have the
modeling ability of Masked Language Models and our experiments demonstrate that
ZeroPrompt is engineering cheap and can be applied to streaming acoustic
encoders on any dataset without any accuracy loss. Specifically, compared with
our baseline models, we achieve 350 $\sim$ 700ms reduction on First Token
Display Time (TDT-F) and 100 $\sim$ 400ms reduction on Last Token Display Time
(TDT-L), with theoretically and experimentally equal WER on both Aishell-1 and
Librispeech datasets.

本文提出了 ZeroPrompt 和 Prompt-and-Refine 策略，这是两种简单而有效的训练免费方法，可在不降低精度的情况下降低流式 ASR 模型的 Token Display Time，并通过在推理期间附加零内容作为提示来促使模型在发音前预测未来标记。实验表明，ZeroPrompt 是成本低廉的，并可以在任何数据集上应用于流式声学编码器，可实现与基线模型相比 350~700ms 的 TDT-F 减少和 100~400ms 的 TDT-L 减少，并且在 Aishell-1 和 Librispeech 数据集上具有理论上和实验上相等的 WER。