Open Challenges in Synthetic Speech Detection
원문 초록
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.
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Pushing the Performance of Synthetic Speech Detection with Kolmogorov-Arnold Networks and Self-Supervised Learning Models
ShiftySpeech: A Large-Scale Synthetic Speech Dataset with Distribution Shifts
SONICS: Synthetic Or Not -- Identifying Counterfeit Songs
Temporal-Channel Modeling in Multi-head Self-Attention for Synthetic Speech Detection
SingFake: Singing Voice Deepfake Detection
The Sound of Silence: Efficiency of First Digit Features in Synthetic Audio Detection
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