AI RESEARCH
Improving Speech Recognition of Named Entities in Classroom Speech with LLM Revision and Phonetic-Semantic Context
arXiv CS.CL
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ArXi:2506.10779v2 Announce Type: replace 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.