End-to-end automatic speech recognition (E2E ASR) systems often suffer from mistranscription of domain-specific phrases, such as named entities, sometimes leading to catastrophic failures in downstream tasks. A family of fast and lightweight named entity correction (NEC) models for ASR have recently been proposed, which normally build on phonetic-level edit distance algorithms and have shown impressive NEC performance. However, as the named entity (NE) list grows, the problems of phonetic confusion in the NE list are exacerbated; for example, homophone ambiguities increase substantially. In view of this, we proposed a novel Description Augmented Named entity CorrEctoR (dubbed DANCER), which leverages entity descriptions to provide additional information to facilitate mitigation of phonetic confusion for NEC on ASR transcription. To this end, an efficient entity description augmented masked language model (EDA-MLM) comprised of a dense retrieval model is introduced, enabling MLM to adapt swiftly to domain-specific entities for the NEC task. A series of experiments conducted on the AISHELL-1 and Homophone datasets confirm the effectiveness of our modeling approach. DANCER outperforms a strong baseline, the phonetic edit-distance-based NEC model (PED-NEC), by a character error rate (CER) reduction of about 7% relatively on AISHELL-1 for named entities. More notably, when tested on Homophone that contain named entities of high phonetic confusion, DANCER offers a more pronounced CER reduction of 46% relatively over PED-NEC for named entities.

翻译：端到端自动语音识别系统常因领域特定短语（如命名实体）的误转录而受损，有时导致下游任务出现灾难性故障。近年提出了一系列基于语音级编辑距离算法、兼具快速与轻量特性的命名实体校正模型，并展现出显著性能。然而，随着命名实体列表规模扩大，列表内的语音混淆问题（如同音词歧义显著增加）愈发突出。针对此，我们提出了一种新颖的描述增强型命名实体校正器（简称DANCER），通过利用实体描述提供额外信息，缓解语音混淆对自动语音识别转录中命名实体校正的影响。为此，我们引入了一种高效实体描述增强掩码语言模型（EDA-MLM），该模型包含稠密检索模块，使掩码语言模型能快速适应领域特定实体以完成命名实体校正任务。在AISHELL-1与同音字数据集上的系列实验验证了建模方法的有效性。在AISHELL-1数据集中，相比基于语音编辑距离的强基线模型（PED-NEC），DANCER针对命名实体的字符错误率相对降低约7%。更显著的是，在包含高语音混淆命名实体的同音字测试集上，DANCER相比PED-NEC实现命名实体字符错误率相对降低达46%。