用大模型修正课堂语音中命名实体识别错误，提升关键信息准确率。
Improving Speech Recognition of Named Entities in Classroom Speech with LLM Revision and Phonetic-Semantic Context
- 结合大模型与音义上下文，自动修正ASR输出的命名实体错误。
- 在MIT课程数据上，命名实体词错误率相对降低30%。
- 适合需要高精度命名实体识别的教育类语音系统开发者。
课堂语音和讲座中常包含人名、专有名词等命名实体（NE），尽管自动语音识别（ASR）在通用语音上表现优异，但对命名实体的词错误率（WER）仍偏高。由于命名实体往往是关键信息，识别错误会严重影响下游应用，尤其当ASR作为复杂系统的前端时。本文提出一种基于大语言模型（LLM）的修订流程，利用LLM的世界知识与推理能力，并结合可用的音素与语义上下文，对ASR预测中的命名实体进行修正。同时构建了NER-MIT-OpenCourseWare数据集，包含45小时麻省理工学院课程录音用于开发与测试。实验结果表明，该方法在该数据集上可实现命名实体部分高达30%的相对词错误率降低。
原文摘要 · Abstract (English)
Classroom speech and lectures often contain named entities (NEs) such as names of people and special terminology. While automatic speech recognition (ASR) systems have achieved remarkable performance on general speech, the word error rate (WER) of state-of-the-art ASR remains high for named entities. Since NE are often the most critical keywords, misrecognizing them can affect all downstream applications, especially when the ASR functions as the front end of a complex system. In this paper, we introduce a large language model (LLM) revision pipeline to revise incorrect NEs in ASR predictions by leveraging not only the LLM's world knowledge and reasoning ability but also the available phonetic and semantic context. We also introduce the NER-MIT-OpenCourseWare dataset, containing 45 hours of data from MIT courses for development and testing. On this dataset, our proposed technique achieves up to 30\% relative WER reduction for NEs.
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