Chinese Automatic Speech Recognition (ASR) error correction presents
significant challenges due to the Chinese language's unique features, including
a large character set and borderless, morpheme-based structure. Current
mainstream models often struggle with effectively utilizing word-level features
and phonetic information. This paper introduces a novel approach that
incorporates a dynamic error scaling mechanism to detect and correct
phonetically erroneous text generated by ASR output. This mechanism operates by
dynamically fusing word-level features and phonetic information, thereby
enriching the model with additional semantic data. Furthermore, our method
implements unique error reduction and amplification strategies to address the
issues of matching wrong words caused by incorrect characters. Experimental
results indicate substantial improvements in ASR error correction,
demonstrating the effectiveness of our proposed method and yielding promising
results on established datasets.

利用动态错误放大机制来检测和纠正自动语音识别输出中的语音错误，通过动态融合单词级特征和语音信息，丰富模型的句义数据，并实施唯一的错误减少和放大策略，以解决因错误字符导致匹配错误单词的问题，实验结果表明在 ASR 错误修正方面取得了重大改进，证明了我们提出方法的有效性，并在已建立的数据集中取得了有希望的结果。

利用动态错误缩放机制提升中文自动语音识别纠错能力

Boosting Chinese ASR Error Correction with Dynamic Error Scaling  Mechanism

While speech recognition Word Error Rate (WER) has reached human parity for
English, continuous speech recognition scenarios such as voice typing and
meeting transcriptions still suffer from segmentation and punctuation problems,
resulting from irregular pausing patterns or slow speakers. Transformer
sequence tagging models are effective at capturing long bi-directional context,
which is crucial for automatic punctuation. Automatic Speech Recognition (ASR)
production systems, however, are constrained by real-time requirements, making
it hard to incorporate the right context when making punctuation decisions.
Context within the segments produced by ASR decoders can be helpful but
limiting in overall punctuation performance for a continuous speech session. In
this paper, we propose a streaming approach for punctuation or re-punctuation
of ASR output using dynamic decoding windows and measure its impact on
punctuation and segmentation accuracy across scenarios. The new system tackles
over-segmentation issues, improving segmentation F0.5-score by 13.9%. Streaming
punctuation achieves an average BLEUscore improvement of 0.66 for the
downstream task of Machine Translation (MT).

本文提出一种基于流式处理的自动标点系统，使用动态解码窗口对 ASR 识别结果进行标点或重新标点，并检测其对标点和分割准确性的影响。实验结果表明，该方法能够有效解决过分割问题，提高 13.9% 的分割 F0.5 评分，并使机器翻译任务的 BLEU 分数平均提高 0.66。