With the development of deep learning, automatic speech recognition (ASR) has
made significant progress. To further enhance the performance, revising
recognition results is one of the lightweight but efficient manners. Various
methods can be roughly classified into N-best reranking methods and error
correction models. The former aims to select the hypothesis with the lowest
error rate from a set of candidates generated by ASR for a given input speech.
The latter focuses on detecting recognition errors in a given hypothesis and
correcting these errors to obtain an enhanced result. However, we observe that
these studies are hardly comparable to each other as they are usually evaluated
on different corpora, paired with different ASR models, and even use different
datasets to train the models. Accordingly, we first concentrate on releasing an
ASR hypothesis revising (HypR) dataset in this study. HypR contains several
commonly used corpora (AISHELL-1, TED-LIUM 2, and LibriSpeech) and provides 50
recognition hypotheses for each speech utterance. The checkpoint models of the
ASR are also published. In addition, we implement and compare several classic
and representative methods, showing the recent research progress in revising
speech recognition results. We hope the publicly available HypR dataset can
become a reference benchmark for subsequent research and promote the school of
research to an advanced level.

通过发布 ASR 假设修正（HypR）数据集和对几种经典的代表性方法进行实现和比较，展示了修正语音识别结果的最新研究进展，希望这些公开可用的 HypR 数据集能够成为进一步研究的参考基准，并推动相关研究领域的发展。