Detecting Partially Spoofed Utterances Without Segment Annotation Through Fake Segment Mining-Based Graph Neural Networks

This paper addresses the problem of detecting partially spoofed utterances (audio that contains both real and fake segments mixed together) without needing labeled data marking where the fake parts are. The researchers propose FMG, a method using Graph Neural Networks (GNNs, a type of AI model that understands relationships between connected pieces of data) to better track how different audio segments relate to each other over time and to identify which segments are likely fake.