# Perceptual implications of automatic anonymization in pathological speech

arXiv: https://arxiv.org/abs/2505.00409 · HTML: https://mlsanity.com/paper/2505.00409/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 31 of its 122 references that are in the map.
- First posted on arXiv 1 May 2025.

## Builds on (most cited first, 30 of 31)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers](https://mlsanity.com/paper/2301.02111/) (2023)
- [FastSpeech 2: Fast and High-Quality End-to-End Text to Speech](https://mlsanity.com/paper/2006.04558/) (2020)
- [High Fidelity Neural Audio Compression](https://mlsanity.com/paper/2210.13438/) (2022)
- [AudioLM: a Language Modeling Approach to Audio Generation](https://mlsanity.com/paper/2209.03143/) (2022)
- [Generalized End-to-End Loss for Speaker Verification](https://mlsanity.com/paper/1710.10467/) (2017)
- [Introducing the VoicePrivacy Initiative](https://mlsanity.com/paper/2005.01387/) (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)
- [The VoicePrivacy 2024 Challenge Evaluation Plan](https://mlsanity.com/paper/2404.02677/) (2024)
- [Speaker anonymisation using the McAdams coefficient](https://mlsanity.com/paper/2011.01130/) (2020)
- [Evaluating Voice Conversion-based Privacy Protection against Informed Attackers](https://mlsanity.com/paper/1911.03934/) (2019)
- [Design Choices for X-vector Based Speaker Anonymization](https://mlsanity.com/paper/2005.08601/) (2020)
- [Anonymizing Speech: Evaluating and Designing Speaker Anonymization Techniques](https://mlsanity.com/paper/2308.04455/) (2023)
- [Speaker anonymization using neural audio codec language models](https://mlsanity.com/paper/2309.14129/) (2023)
- [Anonymizing Speech with Generative Adversarial Networks to Preserve Speaker Privacy](https://mlsanity.com/paper/2210.07002/) (2022)
- [The VoicePrivacy 2022 Challenge Evaluation Plan](https://mlsanity.com/paper/2203.12468/) (2022)
- [Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging](https://mlsanity.com/paper/2302.01622/) (2023)
- [AudVowelConsNet: A Phoneme-Level Based Deep CNN Architecture for Clinical Depression Diagnosis](https://mlsanity.com/paper/2010.16201/) (2020)
- [The Impact of Speech Anonymization on Pathology and Its Limits](https://mlsanity.com/paper/2404.08064/) (2024)
- [Federated learning for secure development of AI models for Parkinson's disease detection using speech from different languages](https://mlsanity.com/paper/2305.11284/) (2023)
- [Automated speech-based screening of depression using deep convolutional neural networks](https://mlsanity.com/paper/1912.01115/) (2019)
- [The IMS Toucan System for the Blizzard Challenge 2023](https://mlsanity.com/paper/2310.17499/) (2023)
- [The effect of speech pathology on automatic speaker verification -- a large-scale study](https://mlsanity.com/paper/2204.06450/) (2022)
- [Anonymising Elderly and Pathological Speech: Voice Conversion Using DDSP and Query-by-Example](https://mlsanity.com/paper/2410.15500/) (2024)
- [Differential privacy enables fair and accurate AI-based analysis of speech disorders while protecting patient data](https://mlsanity.com/paper/2409.19078/) (2024)

## Abstract (arXiv)

Automatic anonymization is increasingly used to enable ethical sharing of clinical speech, yet its perceptual and clinical consequences remain undercharacterized. We present a human-centered evaluation of automatically anonymized pathological speech, using a structured protocol with ten native and non-native German listeners spanning clinical and signal-processing expertise. The cohort comprised 180 German speakers from CLP, Dysarthria, Dysglossia, Dysphonia, and adult and child controls. Each original recording and its automatically-anonymized counterpart was evaluated on four tasks: zero-shot Turing-style discrimination, few-shot discrimination after brief familiarization, 5-point quality rating, and 4-point blinded clinical severity rating by a senior phoniatrician. Listeners detected anonymization at 91% zero-shot and 93% few-shot accuracy, with significant variation across disorders (p=0.008) that attenuated with familiarization. Perceived quality dropped by 30 ppts on a 0-100 scale (p<0.001), reorganizing the perceived-quality hierarchy across groups. Native language modulated detectability but not quality degradation, while domain expertise modulated quality degradation but not detectability, a double dissociation between the two listener attributes; speaker sex and age produced no detectable bias. Clinical severity ratings were preserved at near-perfect agreement in Dysarthria, Dysglossia, and Dysphonia (quadratic-weighted Cohen's kappa 0.87-0.94), with no recording shifting by more than one grade. Crucially, perceptual outcomes were decoupled from the standard computational privacy metric: the pathology with the strongest computational anonymization was the least perceptually conspicuous, and vice versa. These findings argue for disorder-stratified, listener-stratified, clinician-validated evaluation as the minimum standard for licensing anonymized speech for clinical use.