Authors:
Xin Wang, Junichi Yamagishi, Massimiliano Todisco, Hector Delgado, Andreas Nautsch, Nicholas Evans, Md Sahidullah, Ville Vestman, Tomi Kinnunen, Kong Aik Lee, Lauri Juvela, Paavo Alku, Yu-Huai Peng, et al.
Xin Wang, Junichi Yamagishi, Massimiliano Todisco, Hector Delgado, Andreas Nautsch, Nicholas Evans, Md Sahidullah, Ville Vestman, Tomi Kinnunen, Kong Aik Lee, Lauri Juvela, Paavo Alku, Yu-Huai Peng, et al.
Abstract:
The ASVspoof 2019 dataset is a comprehensive collection of bona fide and spoofed speech data designed to support the development and evaluation of anti-spoofing countermeasures in automatic speaker verification (ASV) systems. The dataset addresses the vulnerability of ASV systems to three primary types of spoofing attacks: replay, speech synthesis, and voice conversion. The dataset is divided into two scenarios: Logical Access (LA), which focuses on synthesized and converted speech, and Physical Access (PA), which focuses on replay attacks. The dataset includes various state-of-the-art spoofing techniques to provide a challenging test bed for anti-spoofing research.
The ASVspoof 2019 dataset is a comprehensive collection of bona fide and spoofed speech data designed to support the development and evaluation of anti-spoofing countermeasures in automatic speaker verification (ASV) systems. The dataset addresses the vulnerability of ASV systems to three primary types of spoofing attacks: replay, speech synthesis, and voice conversion. The dataset is divided into two scenarios: Logical Access (LA), which focuses on synthesized and converted speech, and Physical Access (PA), which focuses on replay attacks. The dataset includes various state-of-the-art spoofing techniques to provide a challenging test bed for anti-spoofing research.
Data Creation Method:
Collected and synthesized using various methods, including text-to-speech synthesis, voice conversion, and replay attacks.
Collected and synthesized using various methods, including text-to-speech synthesis, voice conversion, and replay attacks.
Number of Speakers:
- 107 speakers
Total Size:
- Several thousand audio samples
Number of Real Samples:
- 54,000 real (bona fide) samples
Number of Fake Samples:
- 233,960 fake (spoofed) samples
Description of the Dataset:
- Approximately 3-4 seconds per utterance
Extra Details:
The dataset includes logical access and physical access scenarios to cover different types of spoofing attacks. It provides a comprehensive benchmark for the development and testing of anti-spoofing measures.
The dataset includes logical access and physical access scenarios to cover different types of spoofing attacks. It provides a comprehensive benchmark for the development and testing of anti-spoofing measures.
Data Type:
- Audio files
Average Length:
- Approximately 3-4 seconds per utterance
Keywords:
- Automatic speaker verification, countermeasure, anti-spoofing, presentation attack, text-to-speech synthesis, voice conversion, replay, ASVspoof challenge, biometrics, media forensics
When Published:
- July 15, 2020
Annotation Process:
Research on anti-spoofing measures, development and testing of ASV systems, and comparative analysis of different spoofing attack types.
Research on anti-spoofing measures, development and testing of ASV systems, and comparative analysis of different spoofing attack types.
Usage Scenarios:
Logical Access Dataset (for synthetic and converted speech attacks), Physical Access Dataset (for replay attacks)
Logical Access Dataset (for synthetic and converted speech attacks), Physical Access Dataset (for replay attacks)
Miscellaneous Information:
The dataset provides a challenging test bed for anti-spoofing research and includes various state-of-the-art spoofing techniques.
The dataset provides a challenging test bed for anti-spoofing research and includes various state-of-the-art spoofing techniques.
Credits:
Datasets Used:
Datasets Used:
- ASVspoof 2019
Speech Synthesis Models Referenced:
- Various text-to-speech synthesis, voice conversion, and replay attack methods