Lost in Speech: Trilingual Spoken Hallucination Detection Across Audio and Transcripts
While text-based hallucination detection has been extensively studied, spoken hallucination detection remains largely unexplored, particularly for low-resource languages. We present the first multilingual spoken hallucination benchmark comprising 12,013 news samples across English, Russian, and Kazakh with controlled hallucinations of three types and three severity levels. Samples comprise original articles and aligned hallucinated counterparts in text and audio. We complement the synthetic corpus with 290 fact-checked fake news items collected natively in Russian (225) and Kazakh (65), transl
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 48%AI Hallucinations: Why Artificial Intelligence Can Sound Convincing While Getting the Facts Wrong - USA Herald →
- PossiblePossibly related (embedding) · 46%Language Model Hallucination Evaluation with GraphEval - KDnuggets →
- PossiblePossibly related (embedding) · 46%Large Language Models Are Still Getting Stronger, but Researchers Face New Bottlenecks in Data, Evaluation, and Safety | Newswise - Newswise →
- FuzzySimilar title/name (fuzzy) · 87%huggingface/speech-to-speech →
“Fuzzy title match (0.94): “Lost in Speech: Trilingual Spoken Hallucination Detection Ac” ≈ “huggingface/speech-to-speech””
- FuzzyOverlapping authors or contributors · 62%browser-use/browser-use →
“Shared author/contributor keys: lee”
- LinkedLinked via arxiv author · 85%Meruyert Aristombayeva →
“Lost in Speech: Trilingual Spoken Hallucination Detection Across Audio and Transcripts”
- LinkedLinked via arxiv author · 85%Jason S. Lucas →
“Lost in Speech: Trilingual Spoken Hallucination Detection Across Audio and Transcripts”
- LinkedLinked via arxiv author · 85%Chaewan Chun →
“Lost in Speech: Trilingual Spoken Hallucination Detection Across Audio and Transcripts”