  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 para-linguistic
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 para-linguistic 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 three 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. Additionally, we provided a comprehensive evaluation of the
importance of different speech aspects for diagnostics and showed how they are
affected by different types of anonymizers. Lastly, we show the benefits of
using anonymized external data as a data augmentation tool to help recover some
of the COVID-19 diagnostic accuracy loss seen with anonymization.
