SCDF: A Speaker Characteristics DeepFake Speech Dataset for Bias Analysis
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F26%3A0198600" target="_blank" >RIV/00216305:26230/26:0198600 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.18420/biosig_2025_005" target="_blank" >https://doi.org/10.18420/biosig_2025_005</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.18420/biosig_2025_005" target="_blank" >10.18420/biosig_2025_005</a>
Alternative languages
Result language
angličtina
Original language name
SCDF: A Speaker Characteristics DeepFake Speech Dataset for Bias Analysis
Original language description
Despite growing attention to deepfake speech detection, the aspects of bias and fairness remain underexplored in the speech domain. To address this gap, we introduce the Speaker Characteristics Deepfake (SCDF) dataset: a novel, richly annotated resource enabling systematic evaluation of demographic biases in deepfake speech detection. SCDF contains over 237,000 utterances in a balanced representation of both male and female speakers spanning five languages and a wide age range. We evaluate several state-of-the-art detectors and show that speaker characteristics significantly influence detection performance, revealing disparities across sex, language, age, and synthesizer type. These findings highlight the need for bias-aware development and provide a foundation for building non-discriminatory deepfake detection systems aligned with ethical and regulatory standards.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 24th International Conference of the Biometrics Special Interest Group (BIOSIG 2025)
ISBN
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ISSN
1617-5468
e-ISSN
2944-7682
Number of pages
10
Pages from-to
55-64
Publisher name
Gesellschaft für Informatik e.V.
Place of publication
Darmstadt
Event location
Darmstadt
Event date
Sep 24, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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