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English(EN) What Was That Again? Certified Robustness for Automatic Speech Recognition

新流程增强ASR鲁棒性，词错误率降低55%

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种新颖的双门诊断流程，以增强自动语音识别（ASR）系统在对抗性和良性扰动下的鲁棒性。该流程包含一个双面原子审计和一个基于排名的竞赛，旨在认证令牌存在和对抗性排除，从而提高声学安全性。在四种架构上的评估显示，词错误率（WER）相对降低高达55%，并且置信度得分与WER之间的相关性降低。 AI

影响 增强语音识别系统的可靠性和安全性，可能导致在敏感应用中更广泛的采用。

排序理由 该集群包含一篇详细介绍改进ASR系统新方法的学术论文。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新流程增强ASR鲁棒性，词错误率降低55%

报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Andrew C. Cullen, Neil Marchant, Jiani Xie, Paul Montague, Benjamin I. P. Rubinstein ·

    What Was That Again? Certified Robustness for Automatic Speech Recognition

    arXiv:2606.27698v1 Announce Type: cross 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 inc…

  2. arXiv cs.LG TIER_1 English(EN) · Benjamin I. P. Rubinstein ·

    刚才那是什么？自动语音识别的认证鲁棒性

    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 …

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