Update README.md

README.md

@@ -197,7 +197,7 @@ Results shown below can be reproduced using scripts provided in our [GitHub repo
 | FakeOrReal           | 1.000   | 0.992    | 0.994     | 0.989  | 0.991    | 0.005 | 0.011 | 0.63 @ 0.3727        |
 | FakeOrReal-norm      | 0.999   | 0.986    | 0.975     | 0.997  | 0.986    | 0.025 | 0.003 | 0.97 @ 0.7975        |
 | In-the-Wild          | 0.997   | 0.976    | 0.991     | 0.970  | 0.980    | 0.015 | 0.030 | 1.91 @ 0.3240        |
-| Deepfake-Eval-2024  | 0.741   | 0.714    | 0.720     | 0.920  | 0.808    | 0.675 | 0.081 | 33.38 @ 0.9995       |
+| Deepfake-Eval-2024   | 0.797   | 0.743    | 0.734     | 0.920  | 0.816    | 0.545 | 0.083 | 28.73 @ 0.9972       |
 
 You can also fine-tune this model on a specific database, the corresponding code is provided in our [GitHub repository](https://github.com/nii-yamagishilab/AntiDeepfake). Fine-tuning will follow a similar process to training a new model, except that model weights will be initialized as AntiDeepfake checkpoints.
 
@@ -205,11 +205,11 @@ Below are the evaluation results of this model fine-tuned on the Deepfake-Eval-2
 
 | Test Input Length | ROC AUC | Accuracy | Precision | Recall | F1-score | FPR    | FNR    | EER (%) @ Threshold |
 |-------------------|---------|----------|-----------|--------|----------|--------|--------|----------------------|
-| 4s                | 0.8776  | 0.8147   | 0.8126    | 0.9315 | 0.8680   | 0.4060 | 0.0685 | 19.56 @ 0.9238       |
-| 10s               | 0.9362  | 0.8831   | 0.8914    | 0.9346 | 0.9125   | 0.2135 | 0.0654 | 12.10 @ 0.8521       |
-| 13s               | 0.9440  | 0.8977   | 0.9114    | 0.9337 | 0.9224   | 0.1697 | 0.0663 | 10.94 @ 0.8127       |
-| 30s               | 0.9495  | 0.8975   | 0.9310    | 0.9088 | 0.9198   | 0.1233 | 0.0912 | 10.52 @ 0.6061       |
-| 50s               | 0.9414  | 0.8792   | 0.9348    | 0.8707 | 0.9016   | 0.1060 | 0.1293 | 11.37 @ 0.4168       |
+| 4s                | 0.8776  | 0.8147   | 0.8126    | 0.9315 | 0.8680   | 0.4060 | 0.0685 | 19.56 @ 0.9237       |
+| 10s               | 0.9362  | 0.8831   | 0.8914    | 0.9346 | 0.9125   | 0.2135 | 0.0654 | 12.11 @ 0.8521       |
+| 13s               | 0.9440  | 0.8977   | 0.9114    | 0.9337 | 0.9224   | 0.1697 | 0.0663 | 10.95 @ 0.8126       |
+| 30s               | 0.9495  | 0.8975   | 0.9310    | 0.9088 | 0.9198   | 0.1233 | 0.0912 | 10.52 @ 0.6023       |
+| 50s               | 0.9414  | 0.8792   | 0.9348    | 0.8707 | 0.9016   | 0.1060 | 0.1293 | 11.36 @ 0.4097       |
 
 
 
@@ -240,7 +240,7 @@ Below is a breakdown of the training set used for post-training of speech SSL mo
 | LibriTTS-R           | en               | 0              | 583.15      |
 | LibriTTS-Vocoded     | en               | 0              | 2345.14     |
 | LJSpeech             | en               | 23.92          | 0           |
-| MLADD                | 38 languages     | 0              | 377.96      |
+| MLAAD                | 38 languages     | 0              | 377.96      |
 | MLS                  | 8 languages      | 50558.11       | 0           |
 | SpoofCeleb           | Multilingual     | 173.00         | 1916.20     |
 | VoiceMOS             | en               | 0              | 448.44      |