Hierarchical Multi-Path and Multi-Model Selection For Fake Speech Detection

The variety of spoofing algorithms used in generating speech poses obstacles to fake speech detection. Earlier methods have demonstrated complementary effects for detection. This paper proposes a novel hierarchical multi-path multi-model selection method for fake speech detection. It is designed to dynamically select and utilise the most suitable model from a set of complementary models. In our method, four basic detection models are incorporated, each offering partial but complementary detection abilities, to enhance balanced performance on diverse fake speech. The models are trained through a multi-path schema and the selection mechanism is structured hierarchically to improve the generalisation ability. Our method achieves an Equal Error Rate (EER) of 0.37% on the ASVspoof 2019 LA dataset, and outperforms other state-of-the-art method on the cross-domain and cross-dataset scenarios. A statistical analysis of EERs against thirteen unknown attacks reveals our method’s superiority, evidenced by the lowest standard deviation of 0.24, further underscoring our method’s robustness against a range of attacks.