With advances seen in deep learning, voice-based applications are burgeoning, ranging from personal assistants, affective computing, to remote disease diagnostics. As the voice contains both linguistic and paralinguistic information (e.g., vocal pitch, intonation, speech rate, loudness), there is growing interest in voice anonymization to preserve speaker privacy and identity. Voice privacy challenges have emerged over the last few years and focus has been placed on removing speaker identity while keeping linguistic content intact. For affective computing and disease monitoring applications, however, the paralinguistic content may be more critical. Unfortunately, the effects that anonymization may have on these systems are still largely unknown. In this paper, we fill this gap and focus on one particular health monitoring application: speech-based COVID-19 diagnosis. We test two popular anonymization methods and their impact on five different state-of-the-art COVID-19 diagnostic systems using three public datasets. We validate the effectiveness of the anonymization methods, compare their computational complexity, and quantify the impact across different testing scenarios for both within- and across-dataset conditions. Lastly, we show the benefits of anonymization as a data augmentation tool to help recover some of the COVID-19 diagnostic accuracy loss seen with anonymized data.

翻译：随着深度学习的发展，基于语音的应用正在蓬勃发展，范围涵盖个人助理、情感计算到远程疾病诊断。由于语音同时包含语言信息和非语言信息（例如音高、语调、语速、响度），语音匿名化在保护说话者隐私和身份方面日益受到关注。近年来，语音隐私挑战逐渐浮现，研究重点集中在去除说话者身份的同时保持语言内容完整。然而，对于情感计算和疾病监测等应用，非语言内容可能更为关键。不幸的是，匿名化对这些系统的影响在很大程度上仍然未知。在本文中，我们填补了这一空白，并聚焦于一种特定的健康监测应用：基于语音的新冠肺炎诊断。我们测试了两种流行的匿名化方法，并评估了它们对五种最先进的新冠肺炎诊断系统的影响，使用了三个公开数据集。我们验证了匿名化方法的有效性，比较了它们的计算复杂度，并在不同测试场景下（包括数据集内部和跨数据集条件）量化了其影响。最后，我们展示了匿名化作为数据增强工具的优势，有助于恢复因使用匿名化数据而导致的新冠肺炎诊断准确率损失。