Abstract

With the rapid development of speech synthesis and speech conversion technology, methods of spoof speech detection still have problems such as low spoof detection accuracy and poor generality. Therefore, an end-to-end spoof detection method based on context information and attention feature was proposed. Based on Deep Residual Shrinkage Network (DRSN), this method used the Dual-branch Context information Coordination fusion Module (DCCM) to aggregate rich context information, and fused features based on Coordinate Time-Frequency Attention (CTFA) to obtain cross-dimensional interaction features with context information, thus maximizing the potential of capturing artifacts.

Keywords:Spoof speech detection; Context information; Attention feature; End-to-End; Artifacts