An LLM-based revision method with phonetic-semantic context reduces named entity word error rate by up to 30% relative on a new 45-hour MIT classroom speech dataset.
Specifically, we employ a phonetic and semantic matching component to select named entities from the context that are most relevant to those in the ASR prediction
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Improving Speech Recognition of Named Entities in Classroom Speech with LLM Revision and Phonetic-Semantic Context
An LLM-based revision method with phonetic-semantic context reduces named entity word error rate by up to 30% relative on a new 45-hour MIT classroom speech dataset.