# On the Impact of Voice Anonymization on Speech Diagnostic Applications: a Case Study on COVID-19 Detection

arXiv: https://arxiv.org/abs/2304.02181 · HTML: https://mlsanity.com/paper/2304.02181/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 94 references that are in the map.
- First posted on arXiv 5 Apr 2023.

## Builds on (most cited first, 30 of 33)

- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [FastSpeech 2: Fast and High-Quality End-to-End Text to Speech](https://mlsanity.com/paper/2006.04558/) (2020)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [SpeechBrain: A General-Purpose Speech Toolkit](https://mlsanity.com/paper/2106.04624/) (2021)
- [Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis](https://mlsanity.com/paper/1803.09017/) (2018)
- [Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans](https://mlsanity.com/paper/2008.06388/) (2020)
- [The COUGHVID crowdsourcing dataset: A corpus for the study of large-scale cough analysis algorithms](https://mlsanity.com/paper/2009.11644/) (2020)
- [The VoicePrivacy 2020 Challenge: Results and findings](https://mlsanity.com/paper/2109.00648/) (2021)
- [Speaker Anonymization Using X-vector and Neural Waveform Models](https://mlsanity.com/paper/1905.13561/) (2019)
- [Speaker anonymisation using the McAdams coefficient](https://mlsanity.com/paper/2011.01130/) (2020)
- [The INTERSPEECH 2021 Computational Paralinguistics Challenge: COVID-19 Cough, COVID-19 Speech, Escalation & Primates](https://mlsanity.com/paper/2102.13468/) (2021)
- [Evaluating Voice Conversion-based Privacy Protection against Informed Attackers](https://mlsanity.com/paper/1911.03934/) (2019)
- [x-vectors meet emotions: A study on dependencies between emotion and speaker recognition](https://mlsanity.com/paper/2002.05039/) (2020)
- [Design Choices for X-vector Based Speaker Anonymization](https://mlsanity.com/paper/2005.08601/) (2020)
- [Improving Adversarial Robustness via Unlabeled Out-of-Domain Data](https://mlsanity.com/paper/2006.08476/) (2020)
- [Sounds of COVID-19: exploring realistic performance of audio-based digital testing](https://mlsanity.com/paper/2106.15523/) (2021)
- [Probing the Information Encoded in X-vectors](https://mlsanity.com/paper/1909.06351/) (2019)
- [The VoicePrivacy 2022 Challenge Evaluation Plan](https://mlsanity.com/paper/2203.12468/) (2022)
- [Deep Learning Towards Mobile Applications](https://mlsanity.com/paper/1809.03559/) (2018)
- [Virufy: Global Applicability of Crowdsourced and Clinical Datasets for AI Detection of COVID-19 from Cough](https://mlsanity.com/paper/2011.13320/) (2020)
- [COVID-19 and Computer Audition: An Overview on What Speech & Sound Analysis Could Contribute in the SARS-CoV-2 Corona Crisis](https://mlsanity.com/paper/2003.11117/) (2020)
- [An Early Study on Intelligent Analysis of Speech under COVID-19: Severity, Sleep Quality, Fatigue, and Anxiety](https://mlsanity.com/paper/2005.00096/) (2020)
- [Speaker Anonymization with Phonetic Intermediate Representations](https://mlsanity.com/paper/2207.04834/) (2022)
- [IATos: AI-powered pre-screening tool for COVID-19 from cough audio samples](https://mlsanity.com/paper/2104.13247/) (2021)
- [The Second DiCOVA Challenge: Dataset and performance analysis for COVID-19 diagnosis using acoustics](https://mlsanity.com/paper/2110.01177/) (2021)
- [VoiceMask: Anonymize and Sanitize Voice Input on Mobile Devices](https://mlsanity.com/paper/1711.11460/) (2017)
- [Tracking, Profiling, and Ad Targeting in the Alexa Echo Smart Speaker Ecosystem](https://mlsanity.com/paper/2204.10920/) (2022)
- [Detecting Alzheimer's Disease Using Gated Convolutional Neural Network from Audio Data](https://mlsanity.com/paper/1803.11344/) (2018)
- [Audio, Speech, Language, & Signal Processing for COVID-19: A Comprehensive Overview](https://mlsanity.com/paper/2011.14445/) (2020)

## Built on by (most cited first, 1 of 1)

- [WavRx: a Disease-Agnostic, Generalizable, and Privacy-Preserving Speech Health Diagnostic Model](https://mlsanity.com/paper/2406.18731/) (2024)

## Abstract (arXiv)

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.