HALAS is a human-annotated dataset of ASR hallucinations on unprocessed real audio that shows simple metrics outperform current detection methods at 81% ROC-AUC versus 53.1% F1.
Be- yond Transcription: Mechanistic Interpretability in ASR,
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Interleaved SLMs implicitly transcribe spoken words to text tokens in middle layers (top candidate for 77% of data) before predicting in text space and returning to speech.
Internal decoder probing of Whisper yields strongest hallucination detection without references, with late fusion of text and internal features performing best overall.
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Interleaved Speech Language Models Latently Work In Text
Interleaved SLMs implicitly transcribe spoken words to text tokens in middle layers (top candidate for 77% of data) before predicting in text space and returning to speech.