# The effect of speech pathology on automatic speaker verification -- a large-scale study

arXiv: https://arxiv.org/abs/2204.06450 · HTML: https://mlsanity.com/paper/2204.06450/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 63 references that are in the map.
- First posted on arXiv 13 Apr 2022.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [Generalized End-to-End Loss for Speaker Verification](https://mlsanity.com/paper/1710.10467/) (2017)
- [The VoicePrivacy 2020 Challenge: Results and findings](https://mlsanity.com/paper/2109.00648/) (2021)
- [Design Choices for X-vector Based Speaker Anonymization](https://mlsanity.com/paper/2005.08601/) (2020)
- [TristouNet: Triplet Loss for Speaker Turn Embedding](https://mlsanity.com/paper/1609.04301/) (2016)
- [Deep learning methods in speaker recognition: a review](https://mlsanity.com/paper/1911.06615/) (2019)
- [Improving Dysarthric Speech Intelligibility Using Cycle-consistent Adversarial Training](https://mlsanity.com/paper/2001.04260/) (2020)
- [An Empirical Study on Text-Independent Speaker Verification based on the GE2E Method](https://mlsanity.com/paper/2011.04896/) (2020)
- [Deep Neural Network for Automatic Assessment of Dysphonia](https://mlsanity.com/paper/2202.12957/) (2022)

## Built on by (most cited first, 5 of 5)

- [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)
- [Differential privacy enables fair and accurate AI-based analysis of speech disorders while protecting patient data](https://mlsanity.com/paper/2409.19078/) (2024)
- [Factors Affecting the Performance of Automated Speaker Verification in Alzheimer's Disease Clinical Trials](https://mlsanity.com/paper/2306.12444/) (2023)
- [Perceptual implications of automatic anonymization in pathological speech](https://mlsanity.com/paper/2505.00409/) (2025)

## Abstract (arXiv)

Navigating the challenges of data-driven speech processing, one of the primary hurdles is accessing reliable pathological speech data. While public datasets appear to offer solutions, they come with inherent risks of potential unintended exposure of patient health information via re-identification attacks. Using a comprehensive real-world pathological speech corpus, with over n=3,800 test subjects spanning various age groups and speech disorders, we employed a deep-learning-driven automatic speaker verification (ASV) approach. This resulted in a notable mean equal error rate (EER) of 0.89% with a standard deviation of 0.06%, outstripping traditional benchmarks. Our comprehensive assessments demonstrate that pathological speech overall faces heightened privacy breach risks compared to healthy speech. Specifically, adults with dysphonia are at heightened re-identification risks, whereas conditions like dysarthria yield results comparable to those of healthy speakers. Crucially, speech intelligibility does not influence the ASV system's performance metrics. In pediatric cases, particularly those with cleft lip and palate, the recording environment plays a decisive role in re-identification. Merging data across pathological types led to a marked EER decrease, suggesting the potential benefits of pathological diversity in ASV, accompanied by a logarithmic boost in ASV effectiveness. In essence, this research sheds light on the dynamics between pathological speech and speaker verification, emphasizing its crucial role in safeguarding patient confidentiality in our increasingly digitized healthcare era.