# Boosting Chinese ASR Error Correction with Dynamic Error Scaling Mechanism

arXiv: https://arxiv.org/abs/2308.03423 · HTML: https://mlsanity.com/paper/2308.03423/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 28 references that are in the map.
- First posted on arXiv 7 Aug 2023.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Spelling Error Correction with Soft-Masked BERT](https://mlsanity.com/paper/2005.07421/) (2020)
- [A spelling correction model for end-to-end speech recognition](https://mlsanity.com/paper/1902.07178/) (2019)
- [SpellGCN: Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check](https://mlsanity.com/paper/2004.14166/) (2020)
- [Correction of Automatic Speech Recognition with Transformer Sequence-to-sequence Model](https://mlsanity.com/paper/1910.10697/) (2019)
- [Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking](https://mlsanity.com/paper/2105.12306/) (2021)
- [ASR Error Correction and Domain Adaptation Using Machine Translation](https://mlsanity.com/paper/2003.07692/) (2020)
- [FastCorrect: Fast Error Correction with Edit Alignment for Automatic Speech Recognition](https://mlsanity.com/paper/2105.03842/) (2021)
- [Lexicon Enhanced Chinese Sequence Labeling Using BERT Adapter](https://mlsanity.com/paper/2105.07148/) (2021)
- [From Spelling to Grammar: A New Framework for Chinese Grammatical Error Correction](https://mlsanity.com/paper/2211.01625/) (2022)

## Built on by (most cited first, 1 of 1)

- [CantoASR: Prosody-Aware ASR-LALM Collaboration for Low-Resource Cantonese](https://mlsanity.com/paper/2511.04139/) (2025)

## Abstract (arXiv)

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.