All-for-One and One-For-All: Deep learning-based feature fusion for Synthetic Speech Detection
This addresses the problem of detecting speech deepfakes for security applications, but it is incremental as it builds on known features.
The paper tackles synthetic speech detection by fusing three existing feature sets using deep learning, achieving overall better performance than state-of-the-art solutions, with robustness tested across different scenarios and datasets.
Recent advances in deep learning and computer vision have made the synthesis and counterfeiting of multimedia content more accessible than ever, leading to possible threats and dangers from malicious users. In the audio field, we are witnessing the growth of speech deepfake generation techniques, which solicit the development of synthetic speech detection algorithms to counter possible mischievous uses such as frauds or identity thefts. In this paper, we consider three different feature sets proposed in the literature for the synthetic speech detection task and present a model that fuses them, achieving overall better performances with respect to the state-of-the-art solutions. The system was tested on different scenarios and datasets to prove its robustness to anti-forensic attacks and its generalization capabilities.