The paper introduces a corrected protocol for measuring the hackability of speech quality predictors, addressing two key flaws in previous methods. Using this protocol, it finds that four published predictors vary widely in vulnerability, with NISQA being the most hackable. The study also audits an attack‑detect‑patch loop, showing it only hardens predictors within its own attack space and can even reduce performance.
Speech quality predictors are increasingly used as rewards, yet no agreed measure of their hackability exists. The usual measurement has two flaws. First, the perturbation reaches the predictor throug...
arXiv:2606. 06833v1 Announce Type: cross Abstract: Automatic Speech Recognition (ASR) systems operating in real-time settings must process acoustic input under strict temporal constraints, where transcription decisions are inherently made on incomplete information.
The paper introduces the Spectral Masking and Interpolation Attack (SMIA), a black‑box adversarial technique that subtly alters inaudible frequency regions of AI‑generated audio to fool voice authentication systems and their countermeasures. Experiments show SMIA achieves at least 82% success against combined verification and countermeasure systems, 97.5% against standalone speaker verification, and 100% against countermeasures, revealing a critical security gap. The authors argue that current static defenses are inadequate and call for dynamic, context‑aware defenses that can adapt to evolving threats.
arXiv:2610.27210v1 Announce Type: new Abstract: Speech models are increasingly used as an interface to AI systems, from transcription tools to voice agents. Existing audio backdoors require an attack...
arXiv:2606. 27698v1 Announce Type: cross Abstract: Automatic Speech Recognition systems are notoriously both sensitive to adversarial and benign perturbations.
arXiv:2609.27378v1 Announce Type: cross Abstract: End-to-end speech-to-speech dialogue models listen and speak simultaneously, so a continuously open acoustic channel is exposed to adversarial manipu...
arXiv:2609.13150v1 Announce Type: cross Abstract: Reference-free quality predictors such as UTMOS, DNSMOS and SCOREQ are the de facto automatic evaluators for text-to-speech (TTS) and are increasingl...
arXiv:2607. 15724v1 Announce Type: cross Abstract: With the rapid development of deep learning, its vulnerability has gradually emerged in recent years.
arXiv:2606. 05678v1 Announce Type: cross Abstract: Automatic speech recognition (ASR) systems have become widely used for multilingual speech-to-text transcription.
The study investigates which layers of the self‑supervised speech model WavLM carry the most spoof‑relevant information for spoof detection. Using the ASVspoof 5 database, the authors perform per‑layer linear probing, canonical correlation analysis (CCA) with acoustic features, and a grid search over layer subsets, finding that five specific layers outperform pooling all thirteen. CCA shows mid‑layers align with most acoustic features, while voice‑quality metrics correlate more with later layers, and visualizations suggest deeper layers encode general synthesis artifacts rather than attack‑specific signatures.
arXiv:2608.27783v3 Announce Type: replace-cross Abstract: Speech language models (speech LLMs) can generate plausible outputs from audio that contains no usable speech evidence. We study this failure...
arXiv:2607. 01702v1 Announce Type: cross Abstract: Recently, speech classification methods have gained widespread adoption in intelligent gadgets.