Error correction techniques have been used to refine the output sentences
from automatic speech recognition (ASR) models and achieve a lower word error
rate (WER). Previous works usually adopt end-to-end models and has strong
dependency on Pseudo Paired Data and Original Paired Data. But when only
pre-training on Pseudo Paired Data, previous models have negative effect on
correction. While fine-tuning on Original Paired Data, the source side data
must be transcribed by a well-trained ASR model, which takes a lot of time and
not universal. In this paper, we propose UCorrect, an unsupervised
Detector-Generator-Selector framework for ASR Error Correction. UCorrect has no
dependency on the training data mentioned before. The whole procedure is first
to detect whether the character is erroneous, then to generate some candidate
characters and finally to select the most confident one to replace the error
character. Experiments on the public AISHELL-1 dataset and WenetSpeech dataset
show the effectiveness of UCorrect for ASR error correction: 1) it achieves
significant WER reduction, achieves 6.83\% even without fine-tuning and 14.29\%
after fine-tuning; 2) it outperforms the popular NAR correction models by a
large margin with a competitive low latency; and 3) it is an universal method,
as it reduces all WERs of the ASR model with different decoding strategies and
reduces all WERs of ASR models trained on different scale datasets.

自动语音识别（ASR）误差校正中，提出了一种不依赖于训练数据的无监督检测 - 生成 - 选择框架 UCorrect，它能显著减少词语错误率（WER），无需微调可达 6.83%，微调后可达 14.29%，显著优于其他 NAR 修正模型，并具有竞争性的低延迟；同时，它是一种通用方法，可减少不同解码策略和不同规模数据集上训练的 ASR 模型的 WER。