arXiv:2606.27698v1 Announce Type: new Abstract: Automatic Speech Recognition systems are notoriously both sensitive to adversarial and benign perturbations. While this has been repeatedly demonstrated using reference datasets, detecting such behaviors in deployed systems is incredibly challenging, due to the absence of oracle knowledge of the true transcription. We demonstrate that employing a certification-inspired mechanism can significantly decrease WER, increase recall, and decrease the Spearman correlation between confidence and WER. We achieve this through a dual-gate diagnostic pipeline
The paper addresses the persistent vulnerability of Automatic Speech Recognition (ASR) systems to both adversarial and benign perturbations, a critical issue for real-world deployment.
Improved certified robustness for ASR systems can significantly enhance reliability and trust in AI-driven applications, paving the way for broader adoption in sensitive contexts.
The proposed dual-gate diagnostic pipeline offers a concrete method to improve ASR performance metrics like WER and recall, making these systems more dependable in varied environments.
- · AI developers
- · ASR users (e.g., healthcare, finance)
- · Cybersecurity researchers
- · Adversarial attackers
More robust ASR systems will be deployed in critical applications where accuracy and reliability are paramount.
Increased trust in ASR technology could accelerate the development of autonomous AI agents reliant on voice interfaces.
Enhanced ASR security might reduce the effectiveness of voice-based cyberattacks, shifting attacker focus to other modalities.
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Read at arXiv cs.LG