We present AudioNoisePrints, a training-free watermarking pipeline for flow matching and diffusion TTS models, which requires minimal extra computation during inference and does not require retraining the TTS model or reducing the generation quality. We exploited the fact that there are strong correlations between the...
Timothy Tin-Long, Jian Zhu, Aidan Pine et al.· 0 citations
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...
Evaluations show that QACT provides a high-accuracy solution when retraining is affordable, whereas CSVQ offers efficient compression under strict computational budgets.
Zhao-Qing Li, Haoning Xu, Zengrui Jin et al.· IEEE Transactions on Audio,...· 0 citations
This work evaluates speech and audio LLMs as a controlled call-decision problem, and finds that agreement and stacking features improve weaker selectors but do not beat the strongest no-call control.
The Speech-Unsupported Rejection Evaluation Challenge (SURE-Challenge) is defined for this admission step and identifies a pre-generation error mode missed by answer-only scoring.
Mengzhe Geng· 0 citations
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