Decoder-based LLMs achieve 92-94% agreement with human annotators for ASR hypothesis selection on HATS, substantially outperforming WER (63%) and embedding-based semantic metrics.
2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages=
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Evaluation of Automatic Speech Recognition Using Generative Large Language Models
Decoder-based LLMs achieve 92-94% agreement with human annotators for ASR hypothesis selection on HATS, substantially outperforming WER (63%) and embedding-based semantic metrics.