GNCL: A Graph Neural Network with Consistency Loss for Segment-Level Spoofed Speech Detection

Abstract

Segment-level spoofed speech detection focuses on recognizing fake or synthetic segments within identifying partially spoofed speech. Nevertheless, existing models for this segment-level task usually overlook latent local relationships between fake and bona fide segments, and further, a lack of inter-branch consistency may lead to insufficient information sharing between different domains. In this regard, we propose an approach of a Graph Neural network with Consistency Loss (GNCL) for segment-level spoofed speech detection. The proposed approach contains a speech representation extraction module, a graph neural network module for modeling local differences, and a consistency-enhanced loss function. Experimental evaluations on the partial spoof dataset demonstrate that, the proposed approach outperforms compared approaches in spoofed-segment detection in terms of the equal error rate, showcasing its effectiveness for the segment-level spoofed speech detection.