DGPN: A Dual Graph Prototypical Network for Few-Shot Speech Spoofing Algorithm Recognition

As synthetic speech technologies rapidly advance, accurately classifying these synthesis algorithms has become increasingly critical in the speech anti-spoofing. Nevertheless, in the incipient stage of emerging spoofing algorithms, the acquisition of ample generated speech samples is often constrained, impeding the efficacy of conventional models. To this end, we introduce a novel methodology within the realm of few-shot learning, named Dual Graph Prototypical Network (DGPN), in view of this limitation for the Speech Spoofing Algorithm Recognition (SSAR) task. The proposed method consists of intra-speech graph and inter-speech graph modules, where the former employs graph attention networks to model the low-level representations of an utterance, and the latter utilizes graph neural networks to depict high-level representations of different utterances. Experimental evaluations demonstrate that the proposed method outperforms existing models in classification accuracy, showcasing its effectiveness in addressing the challenge of the few-shot SSAR task.