从朗读语音到口语数字：针对知情攻击者的语音隐私任务特定评估
From Read Speech to Spoken Digits: A Task-Specific Evaluation of Speech Privacy With Informed Attackers
AI总结：
研究针对知情攻击者，以数字识别为场景评估三种语音混淆技术保护语音隐私的有效性，用通用语音识别模型等作基线，发现数字模态等因素影响识别性能，强调需更全面应用导向的评估方法。
AI中文摘要：
保护现实生活录音中的语音隐私日益受到关注。本研究以数字识别为特定任务且具实际动机的评估场景，评估三种混淆技术保护语言语音内容的有效性。作为首个基线，将通用语音识别模型和数字特定分类器用作知情攻击者来识别单个数字和拼接数字序列。实验结果表明，数字模态、语速和攻击模型的识别性能存在显著差异。这些发现强调需要更全面且面向应用的评估方法来确保语音隐私。
英文摘要：
Protecting speech privacy in real-life audio recordings is a growing concern. This contribution evaluates the effectiveness of three obfuscation techniques in protecting linguistic speech content, using digit recognition as a task-specific and practically motivated evaluation scenario. As a first baseline, a general-purpose speech recognition model and a digit-specific classifier were applied as informed attackers to recognise both single digits and concatenated digit sequences. Our experimental results demonstrate significant differences in recognition performance across digit modality, speech rate, and attack model. These findings emphasize the need for more comprehensive and application-oriented evaluation methods to ensure speech privacy.
