Source Tracing of Audio Deepfake Systems: MLAAD Source Tracing Protocol
<p>This data was created as part of the work entitled "Source Tracing of Audio Deepfake Systems", published in Interspeech 2024.</p> <p>In doing our training, development, and testing, Pindrop used WAV files from the Multi-Language Audio Anti-Spoof Dataset (MLAAD) and the M-AILABS Speech Dataset. You can obtain copies of these same WAV files directly from the developers here:<br>MLAAD (version 1): <a href="https://owncloud.fraunhofer.de/index.php/s/tL2Y1FKrWiX4ZtP#editor" target="_blank" rel="noopener">https://owncloud.fraunhofer.de/index.php/s/tL2Y1FKrWiX4ZtP#editor</a><br>M-AILABS: <a href="https://www.caito.de/2019/01/03/the-m-ailabs-speech-dataset/" target="_blank" rel="noopener">https://www.caito.de/2019/01/03/the-m-ailabs-speech-dataset/</a></p>
ShareScore
20/100
Overall dataset sharing score
Score breakdown
These five areas show where the dataset supports — or may limit — practical reuse.
- Stewardship
- 4
- Harmonization
- 4
- Access
- 8
- Reuse readiness
- 0
- Engagement
- 4