Detecting Speech AI Hallucinations from Attention Patterns: Tested on Qwen-2-Audio and Voxtral-3B
burkov · x · 2026-10-18
Speech AI models can hallucinate during ASR and speech translation, producing fluent but ungrounded outputs that mislead users and downstream systems. Existing detection methods require reference transcriptions or costly manual annotation, and text-LLM detection techniques don't transfer directly to audio.
This study explores using the model's internal attention patterns as a cheap, automatic hallucination signal. The authors experimented with Qwen-2-Audio and Voxtral-3B on ASR and speech-to-text translation tasks using public datasets, testing whether attention patterns reveal when generated words stray from the audio input.
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