MLAAD: The Multi-Language Audio Anti-Spoofing Dataset

We present the MLAAD dataset, which is a multi-language dataset for the task of audio anti-spoofing. This dataset has been created using a diverse set of text-to-speech (TTS) models, and is designed to evaluate the out-of-domain generalization of anti-spoofing systems, both with respect to new languages, as well as new TTS models. Specifically, MLAAD comprises:

The dataset is supposed to be used in conjunction with the M-AILABS dataset. MLAAD provides only the synthetic audio, while M-AILABS provides the real audio.


Figure 1: Data creation pipeline.


How to cite:
  title={MLAAD: The Multi-Language Audio Anti-Spoofing Dataset},
  author={M{\"u}ller, Nicolas M and Kawa, Piotr and Choong, Wei Herng and Casanova, Edresson and G{\"o}lge, Eren and M{\"u}ller, Thorsten and Syga, Piotr and Sperl, Philip and B{\"o}ttinger, Konstantin},
  journal={International Joint Conference on Neural Networks (IJCNN)},