Open Challenges in Synthetic Speech Detection
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Abstract (arXiv)
In this paper the current status and open challenges of synthetic speech detection are addressed. The work comprises an initial analysis of available open datasets and of existing detection methods, a description of the requirements for new research datasets compliant with regulations and better representing real-case scenarios, and a discussion of the desired characteristics of future trustworthy detection methods in terms of both functional and non-functional requirements. Compared to other works, based on specific detection solutions or presenting single dataset of synthetic speeches, our paper is meant to orient future state-of-the-art research in the domain, to quickly lessen the current gap between synthesis and detection approaches.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Sep 2022.
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Citing papers per year: 2024: 3; 2025: 1; 2026: 1.
5 papers.
- 2024Leveraging Mixture of Experts for Improved Speech Deepfake Detectioncited by 14
- 2024Easy, Interpretable, Effective: openSMILE for voice deepfake detectioncited by 5
- 2024Analyzing the Impact of Splicing Artifacts in Partially Fake Speech Signalscited by 4
- 2025Anomaly Detection and Localization for Speech Deepfakes via Feature Pyramid Matchingcited by 4
- 2026Explainable-by-Design Audio Deepfake Detection via Wiener-Hopf Linear Predictioncited by 0
- 2026Explainable-by-Design Audio Deepfake Detection via Wiener-Hopf Linear Prediction14 Jul 2026
- 2025Anomaly Detection and Localization for Speech Deepfakes via Feature Pyramid Matching23 Mar 2025
- 2024Leveraging Mixture of Experts for Improved Speech Deepfake Detection24 Sep 2024
- 2024Easy, Interpretable, Effective: openSMILE for voice deepfake detection28 Aug 2024
- 2024Analyzing the Impact of Splicing Artifacts in Partially Fake Speech Signals25 Aug 2024