  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.
