MLAAD: The Multi-Language Audio Anti-Spoofing Dataset

Nicolas M. Müller; Piotr Kawa; Wei Herng Choong; Edresson Casanova; Eren Gölge; Thorsten Müller; Piotr Syga; Philip Sperl; Konstantin Böttinger

arXiv · 2401.09512

MLAAD: The Multi-Language Audio Anti-Spoofing Dataset

Nicolas M. Müller·Piotr Kawa·Wei Herng Choong·Edresson Casanova·Eren Gölge·Thorsten Müller·Piotr Syga·Philip Sperl·Konstantin Böttinger

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Abstract

This paper presents the Multi-Language Audio Anti-Spoofing Dataset (MLAAD), version 11: a dataset of synthetic audio to train and evaluate audio deepfake detection models. It features 205 Text-to-Speech (TTS) models, comprising a total of 1153.6 hours of synthetic voice in 54 different languages. To evaluate this dataset, we train three state-of-the-art deepfake detection models with MLAAD and observe that it demonstrates superior performance to comparable datasets like InTheWild and FakeOrReal when used as a training resource. Moreover, compared to the renowned ASVspoof 2019 dataset, MLAAD proves to be a complementary resource. In tests across eight datasets, MLAAD and ASVspoof 2019 alternately outperformed each other, each excelling on four datasets. By publishing the dataset and making a trained model accessible via an interactive webserver, we aim to democratize anti-spoofing technology, making it accessible beyond the realm of specialists, and contributing to global efforts against audio spoofing and deepfakes.

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Nicolas M. Müller, Piotr Kawa, Wei Herng Choong, Edresson Casanova, Eren Gölge, Thorsten Müller, Piotr Syga, Philip Sperl, Konstantin Böttinger. 2026-08-28. MLAAD: The Multi-Language Audio Anti-Spoofing Dataset. https://arxiv.org/abs/2401.09512

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