HALAS: A Human-Annotated Dataset of Hallucinations of Modern ASR Systems
End-to-end Automatic Speech Recognition (ASR) systems hallucinate on natural speech, yet existing mitigation methods are typically evaluated on non-speech or artificially corrupted audio. We introduce HALAS, the first human-annotated dataset of naturally occurring hallucinations from seven state-of-the-art ASR models on real unprocessed earnings call recordings. HALAS provides span-level labels, enabling analysis of hallucination patterns and their severity. Our analysis reveals strong cross-model vocabulary overlap and confirms that hallucinations also occur for almost correctly transcribed speech (characterized by a low Word Error Rate). The proposed benchmark with HALAS shows that the ...
| Authors | Mateusz Barański, Jan Jasiński, Julitta Bartolewska et al. |
|---|---|
| Primary category | cs.SD |
| Published | 22 June 2026 |
| Last updated at source | 22 June 2026 |
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