A Data Perspective on Ethical Challenges in Voice Biometrics Research
Bibliographic record
Abstract
Speaker recognition technology, deployed in sectors like banking, education, recruitment, immigration, law enforcement, and healthcare, relies heavily on biometric data. However, the ethical implications and biases inherent in the datasets driving this technology have not been fully explored. Through a longitudinal study of close to 700 papers published at the ISCA Interspeech Conference in the years 2012 to 2021, we investigate how dataset use has evolved alongside the widespread adoption of deep neural networks. Our study identifies the most commonly used datasets in the field and examines their usage patterns. The analysis reveals significant shifts in data practices since the advent of deep learning: a small number of datasets dominate speaker recognition training and evaluation, and the majority of studies evaluate their systems on a single dataset. For four key datasets–Switchboard, Mixer, VoxCeleb, and ASVspoof–we conduct a detailed analysis of metadata and collection methods to assess ethical concerns and privacy risks. Our study highlights numerous challenges related to sampling bias, re-identification, consent, disclosure of sensitive information and security risks in speaker recognition datasets, and emphasizes the need for more representative, fair, and privacy-aware data collection in this domain.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.259 | 0.446 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.008 | 0.034 |
| Scholarly communication | 0.025 | 0.029 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".