Full-text Error Correction for Chinese Speech Recognition with Large Language Model
<|reference_start|>Full-text Error Correction for Chinese Speech Recognition with Large Language Model: Large Language Models (LLMs) have demonstrated substantial potential for error correction in Automatic Speech Recognition (ASR). However, most research focuses on utterances from short-duration speech recordings, which are the predominant form of speech data for supervised ASR training. This paper investigates the effectiveness of LLMs for error correction in full-text generated by ASR systems from longer speech recordings, such as transcripts from podcasts, news broadcasts, and meetings. First, we develop a Chinese dataset for full-text error correction, named ChFT, utilizing a pipeline that involves text-to-speech synthesis, ASR, and error-correction pair extractor. This dataset enables us to correct errors across contexts, including both full-text and segment, and to address a broader range of error types, such as punctuation restoration and inverse text normalization, thus making the correction process comprehensive. Second, we fine-tune a pre-trained LLM on the constructed dataset using a diverse set of prompts and target formats, and evaluate its performance on full-text error correction. Specifically, we design prompts based on full-text and segment, considering various output formats, such as directly corrected text and JSON-based error-correction pairs. Through various test settings, including homogeneous, up-to-date, and hard test sets, we find that the fine-tuned LLMs perform well in the full-text setting with different prompts, each presenting its own strengths and weaknesses. This establishes a promising baseline for further research. The dataset is available on the website.<|reference_end|>
@article{tang2024full-text,
    title={Full-text Error Correction for Chinese Speech Recognition with Large
  Language Model},
    author={Zhiyuan Tang, Dong Wang, Shen Huang, Shidong Shang},
    journal={arXiv preprint arXiv:2409.07790},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.07790},
    primaryClass={cs.CL eess.AS}
}