Real Additive Margin Softmax for Speaker Verification
원문 초록
The additive margin softmax (AM-Softmax) loss has delivered remarkable performance in speaker verification. A supposed behavior of AM-Softmax is that it can shrink within-class variation by putting emphasis on target logits, which in turn improves margin between target and non-target classes. In this paper, we conduct a careful analysis on the behavior of AM-Softmax loss, and show that this loss does not implement real max-margin training. Based on this observation, we present a Real AM-Softmax loss which involves a true margin function in the softmax training. Experiments conducted on VoxCeleb1, SITW and CNCeleb demonstrated that the corrected AM-Softmax loss consistently outperforms the original one. The code has been released at https://gitlab.com/csltstu/sunine.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Pushing the Performance of Synthetic Speech Detection with Kolmogorov-Arnold Networks and Self-Supervised Learning Models
ASVspoof2019 vs. ASVspoof5: Assessment and Comparison
Pushing the Frontiers of Self-Distillation Prototypes Network with Dimension Regularization and Score Normalization
SSPS: Self-Supervised Positive Sampling for Robust Self-Supervised Speaker Verification
CBW: Towards Dataset Ownership Verification for Speaker Verification via Clustering-based Backdoor Watermarking
Self-Supervised Frameworks for Speaker Verification via Bootstrapped Positive Sampling
토론
이 논문과 연결된 포럼 글이 아직 없습니다.