FairSSD: Understanding Bias in Synthetic Speech Detectors
Open accessAbstract
Methods that can generate synthetic speech which is perceptually indistinguishable from speech recorded by a human speaker, are easily available. Several incidents report misuse of synthetic speech generated from these methods to commit fraud. To counter such misuse, many methods have been proposed to detect synthetic speech. Some of these detectors are more interpretable, can generalize to detect synthetic speech in the wild and are robust to noise. However, limited work has been done on understanding bias in these detectors. In this work, we examine bias in existing synthetic speech detectors to determine if they will unfairly target a particular gender, age and accent group. We also inspect whether these detectors will have a higher misclassification rate for bona fide speech from speech-impaired speakers w.r.t fluent speakers. Extensive experiments on 6 existing synthetic speech detectors using more than 0.9 million speech signals demonstrate that most detectors are gender, age and accent biased, and future work is needed to ensure fairness. To support future research, we release our evaluation dataset, models used in our study and source code at https://gitlab.com/viper-purdue/fairssd.
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SSD systems are biased, exhibiting higher false-positive rates for bona fide speech from male speakers, adolescents, the elderly, and speakers with South Asian and Australian English accents.
“The results revealed that SSD systems are biased, exhibiting higher false-positive rates for bona fide speech from male speakers, adolescents, and the elderly, as well as speakers with South Asian and Australian English accents.”
From Audio Deepfake Detection: What Has Been Achieved and What Lies Ahead (2025) · cites this paper as [158]
SSD systems are particularly unfair to speech-impaired speakers.
“Additionally, SSD systems were found to be particularly unfair to speech-impaired speakers.”
From Audio Deepfake Detection: What Has Been Achieved and What Lies Ahead (2025) · cites this paper as [158]
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- Authors
- Singh Yadav, A. K., Bhagtani, K., Salvi, D., Bestagini, P., & Delp, E. J.
- Affiliations
- Purdue University,Video and Image Processing Lab (VIPER),West Lafayette,Indiana,USA
- Purdue University West Lafayette (US)
- Informazione e Bioingegneria,Dipartimento di Elettronica,Milano,Italy
- Venue
- 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Publisher
- IEEE
- Published
- 2024-06-17
- Pages
- 4418-4428
- OpenAlex
- W4402917138
- Cited by
- 12
- References
- 67
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 2
- FWCI
- 3.46
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Air Force Research Laboratory
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- synthetic speech detection, algorithmic bias, fairness, gender bias, age bias, accent bias, speech disorders, noise robustness, evaluation dataset
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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