Speaker Identification on VoxCeleb1 (test)
96.25Top-1 AccuracyTARNet
Evaluation Results
| Method | Links | |||||
|---|---|---|---|---|---|---|
| TARNet2026.05 | 96.25 | 98.91 | 96.49 | 96.25 | 95.78 | |
| ECAPA-TDNNBackend=Linear classification layer2026.05 | 94.5 | 98.32 | 94.29 | 94.5 | 94.39 | |
| x-vectorBackend=Linear classification layer2026.05 | 91.89 | 97.67 | 92.06 | 91.89 | 91.97 | |
| DLSI-SM-VGG-M2026.05 | 90.04 | 97.2 | 89.79 | 90.04 | 89.91 | |
| ResNeXt2026.05 | 88.83 | 96.81 | 88.69 | 88.83 | 88.76 | |
| Thin ResNet-342026.05 | 86.85 | 96.22 | 86.97 | 86.85 | 86.91 | |
| VGG-CNN2026.05 | 86.14 | 95.76 | 86.4 | 86.14 | 86.27 | |
| LF-DNN-GCC2026.05 | 83.34 | 94.12 | 83.53 | 83.34 | 83.43 | |
| DCSA-ResNet182026.05 | 81.88 | 93.54 | 81.68 | 81.88 | 81.78 | |
| VGG-M2026.05 | 80.81 | 92.93 | 80.63 | 80.81 | 80.72 | |
| ResNetSE-34L2026.05 | 77.45 | 90.17 | 77.18 | 77.45 | 77.31 | |
| VGG-M-402026.05 | 73.11 | 88.96 | 73.26 | 73.11 | 73.18 | |
| CNN-no-norm2026.05 | 67.59 | 85.3 | 67.81 | 67.59 | 67.7 |