csun22 / synthetic-voice-detection-vocoder-artifacts Goto Github PK

Hello
Thanks for dataset, can you release pre-trained models for vocoder detection system?

Regards
Ajinkya

I have some questions regarding the evaluation metrics and results presented in Sections 4.4 and 4.5.

Intra-dataset Evaluation (Section 4.4)

The paper reports a very low EER of 0.19% on the WaveFake dataset using the RawNet2 model.

• To confirm my understanding, was this evaluation performed with the model being trained and tested on the same WaveFake dataset?

Cross-dataset Evaluation (Section 4.5)

On the other hand, the EER significantly increased to 26.95% when the model trained on the LibriSeVoc dataset was tested on the WaveFake dataset. This suggests poor generalization to unseen data.

• Are there any ongoing efforts to improve this aspect of the model, perhaps through domain adaptation techniques or exposure to a more diverse set of vocoders during training?

Thank you for the awesome project. Could you provide a full script to me ([email protected]), I would like to do some experiment.

Thanks

Hi,
Thanks for all the great work. You said you will put pre-train model in core_script folder in reply of another issue. But I can't find it

Hello,

Thank you for sharing this dataset.

Would it be possible to have more information on the generation of audios? In particular, the names of the vocoders training datasets used.

Thank you.

Tony

Where is the train.py script and other remaining scripts?

I found your paper here: https://arxiv.org/pdf/2304.13085

Thanks for your contribution!

Can you provide more detail description or sample source code for WavLM in your paper?

I have tried to reproduce it but the result seen not to be potential!

P/S: Do you know which is the best EER performing on ASVSpoof2019?

I'm interested in your CVPR paper and would like to replicate your experiments. Could you please upload the training and evaluation code?