SURE-Voice: A Front-End Baseline for Speech-Evidence Filtering in Speech LLMs
Speech language models (speech LLMs) can generate plausible outputs from audio that contains no usable speech evidence. We study this failure as a pre-generation support-estimation problem and present SURE-Voice, a training-free front end that decides whether an audio prompt contains intelligible speech evidence before calling a speech LLM. We build SURE-Challenge with a 640-example SURE-Core split and a 1,920-example SURE-Extended split derived from 120 LibriSpeech source utterances. Using one fixed operating point, an energy screen plus Whisper token confidence raises unsupported accuracy on the held-out Extended test from 0.000--0.133 to 0.919 for six non-degenerate speech LLM backbone...
| Authors | Mengzhe Geng |
|---|---|
| Primary category | eess.AS |
| Published | 27 August 2026 |
| Last updated at source | 14 September 2026 |
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