SURE-Challenge: Evaluating Speech Evidence Before Speech-LLM Generation
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Electrical Engineering and Systems Science > Audio and Speech Processing
arXiv:2608.27783 (eess)
[Submitted on 27 Aug 2026]
Title:SURE-Challenge: Evaluating Speech Evidence Before Speech-LLM Generation
Authors:Mengzhe Geng
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Abstract:Speech LLMs are usually graded after they answer, although an operating system first has to decide whether a waveform should be sent to the model. We define the Speech-Unsupported Rejection Evaluation Challenge (SURE-Challenge) for this admission step. The benchmark pairs LibriSpeech-derived transcription and first-word question answering with unsupported silence, colored noise, synthetic tones, and source-ambiguous babble under disjoint source splits. Front-end ablations use Qwen2-Audio; the selected energy-plus-Whisper-score rule is then replayed before six speech/audio LLMs. On the 474-row leakage-screened SURE-Extended test set, raw Qwen2-Audio rejects 15/204 unsupported inputs, whereas the fixed rule rejects 196/204 and leaves supported accuracy unchanged. External checks delimit this number: Common Voice retention drops as the Whisper-score threshold is tightened, and no-speed babble gives 18 to 24 rejected clips out of 54 across regenerated seeds. The result identifies a pre-generation error mode missed by answer-only scoring.
| Subjects: | Audio and Speech Processing (eess.AS); Computation and Language (cs.CL); Sound (cs.SD) |
| Cite as: | arXiv:2608.27783 [eess.AS] |
| (or arXiv:2608.27783v1 [eess.AS] for this version) | |
| https://doi.org/10.48550/arXiv.2608.27783
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled SURE-Challenge: Evaluating Speech Evidence Before Speech-LLM Generation, by Mengzhe Geng
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