  Recent studies have demonstrated the efficacy of large language models (LLMs)
in error correction for automatic speech recognition (ASR). However, much of
the research focuses on the English language. This paper redirects the
attention to Chinese. Firstly, we construct a specialized benchmark dataset
aimed at error correction for Chinese ASR with 724K hypotheses-transcription
pairs, named the Chinese Hypotheses Paradise dataset (ChineseHP), which
contains a wide range of scenarios and presents significant challenges.
Subsequently, we conduct a preliminary evaluation using the dataset for both
direct-prompting and fine-tuning pre-trained LLMs. Furthermore, we propose a
straightforward method of Pinyin regularization for prompts, which involves the
transcription of Pinyin directly from text hypotheses. The experimental results
reveal that Pinyin regularization consistently enhances the error-correcting
ability of LLMs when compared with those without regularization. The dataset is
available on the website.
