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。

UCorrect：一种无监督的自动语音识别错误修正框架

UCorrect: An Unsupervised Framework for Automatic Speech Recognition  Error Correction

In this paper, we aim to create weak alignment supervision to aid the
end-to-end modeling. Towards this end, we use the existing hybrid ASR system to
produce triphone alignments of the training audios. We then create a
cross-entropy loss at a certain layer of the encoder using the derived
alignments. In contrast to the general one-hot cross-entropy losses with or
without loss weighting, here we use a cross-entropy loss with a label smoothing
parameter to regularize the supervision. As a comparison, we also conduct the
experiments with one-hot cross-entropy losses and CTC losses with loss
weighting. The results show that placing the weak alignment supervision with
the label smoothing parameter of 0.5 at the third encoder layer outperforms the
other two approaches and leads to about 5% relative WER reduction on the
TED-LIUM 2 dataset over the baseline. We see similar improvements when applying
the method out-of-the-box on a Tagalog end-to-end ASR system.

本文旨在创建弱对齐监督以辅助端到端建模，在已有的混合 ASR 系统中产生训练音频的三音素对齐。我们在编码器的某一层上使用这些对齐创建交叉熵损失。与一般的一位有效交叉熵损失或带有损失加权的方法不同，我们在此使用带有标签平滑参数的交叉熵损失来规范监督。与使用一位有效交叉熵损失和带有损失加权的 CTC 损失进行实验相比，结果显示将弱对齐监督与标签平滑参数 0.5 放置在第三个编码器层上的方法优于另外两种方法，在 TED-LIUM 2 数据集上相对词错误率减小约 5%。将该方法直接应用于一个塔加路语端到端 ASR 系统时也得到类似的改进。