Synthesized Speech Attribution Using The Patchout Spectrogram Attribution Transformer

Abstract

The malicious use of synthetic speech has increased with the recent availability of speech generation tools. It is important to determine whether a speech signal is authentic (spoken by a human) or is synthesized and to determine the generation method used to create it. Identifying the synthesis method is known as synthetic speech attribution. In this paper, we propose the use of a transformer deep learning method that analyzes mel-spectrograms for synthetic speech attribution. Our method known as Patchout Spectrogram Attribution Transformer (PSAT) can distinguish new, unseen speech generation methods from those seen during training. PSAT demonstrates high performance in attributing synthetic speech signals. Evaluation on the DARPA SemaFor Audio Attribution Dataset and the ASVSpoof2019 Dataset shows that our method achieves more than 95% accuracy in synthetic speech attribution and performs better than existing deep learning approaches.