PartialSpoof Database - Partially Spoofed Audio Dataset for Anti-spoofing
- 1. National Institute of Informatics
- 2. Digital Security Department, EURECOM
Description
All existing databases of spoofed speech contain attack data that is spoofed in its entirety. In practice, it is entirely plausible that successful attacks can be mounted with utterances that are only partially spoofed. By definition, partially-spoofed utterances contain a mix of both spoofed and bona fide segments, which will likely degrade the performance of countermeasures trained with entirely spoofed utterances. This hypothesis raises the obvious question: ‘Can we detect partially spoofed audio?’ This paper introduces a new database of partially-spoofed data, named PartialSpoof, to help address this question. This new database enables us to investigate and compare the performance of countermeasures on both utterance- and segmental- level labels. Experimental results using the utterance-level labels reveal that the reliability of countermeasures trained to detect fully-spoofed data is found to degrade substantially when tested with partially-spoofed data, whereas training on partially-spoofed data performs reliably in the case of both fully- and partially- spoofed utterances. Additional experiments using segmental-level labels show that spotting injected spoofed segments included in an utterance is a much more challenging task even if the latest countermeasure models are used.
- !!!NEW!!! For detailed (bonafide/spoofing methods/nonspeech/concatenated parts) timestamps of PartialSpoof v1.3
- Google Drive
- The official version is under preparation. Please download this one if you urgently need it.
- For fine-grained labels of PartialSpoof v1.2
- Arxiv: http://arxiv.org/abs/2204.05177
- PartialSpoof Database v1.2 (including segmental-level labels in different temporal resolutions and timestamp labels): This one
- For the multi-task version of PartialSpoof v1.1
- Arxiv: https://arxiv.org/abs/2107.14132
- PartialSpoof Database v1.1 (including 0.16s segmental level labels): https://zenodo.org/record/5112031
- For the initial version of PartialSpoof v1.0
- Arxiv: https://arxiv.org/abs/2104.02518
- Samples: https://nii-yamagishilab.github.io/zlin-demo/IS2021/index.html
- PartialSpoof Database v1.0: https://zenodo.org/record/4817532
P.S.
1. Compared to the PartialSpoof_v1.0 and PartialSpoof_v1.1, only database_segment_labels_v1.2.tar.gz, database_vad.tar.gz, and README_v1.2 are updated for version 1.2, you don't need to download other files if you already downloaded version1.0 or 1.1.
2. File database_eval.tar.gz is a little large, if you cannot download it smoothly, you can download the split database_eval.tar.gz from PartialSpoof_v1.0
Notes
Files
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Additional details
Related works
- Cites
- Dataset: https://datashare.ed.ac.uk/handle/10283/3336 (URL)
- Is part of
- Conference paper: arXiv:2104.02518 (arXiv)
- Is supplement to
- Dataset: https://zenodo.org/record/4817532#.YLO07S2l1hE (URL)
References
- Zhang, L., Wang, X., Cooper, E., Yamagishi, J., Patino, J., & Evans, N. (2021). An Initial Investigation for Detecting Partially Spoofed Audio. arXiv preprint arXiv:2104.02518.
- Wang, X., Yamagishi, J., Todisco, M., Delgado, H., Nautsch, A., Evans, N., ... & Ling, Z. H. (2020). ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech. Computer Speech & Language, 64, 101114.
- Zhang, L., Wang, X., Cooper, E., Yamagishi, J. (2021) Multi-task Learning in Utterance-level and Segmental-level Spoof Detection. Proc. ASVspoof2021 workshop, 9-15,
- Zhang, L., Wang, X., Cooper, E., Evans, N., & Yamagishi, J. (2022). The PartialSpoof Database and Countermeasures for the Detection of Short Generated Audio Segments Embedded in a Speech Utterance. arXiv preprint arXiv:2204.05177.