Spoofing and countermeasures for speaker verification: A survey
By Zhizheng Wu, N. Evans, Tomi H. Kinnunen, Junichi Yamagishi, Federico Alegre, Haizhou Li. Speech Communication, 2015
705 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Signal Processing
View Paper: https://www.semanticscholar.org/paper/4de74a4c970a9f7492ee93e4b06a210c08d2bc08
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Junichi Yamagishi
- N. Evans
- Massimiliano Todisco
- Héctor Delgado
- Xin Eric Wang
- Tomi H. Kinnunen
- Kong Aik LEE
- Andreas Nautsch
In Signal Processing
- N. Evans
- Junichi Yamagishi
- Massimiliano Todisco
- Héctor Delgado
- Xin Eric Wang
- Tomi H. Kinnunen
- Kong Aik LEE
- Andreas Nautsch
Citations in the corpus
- A New Feature for Automatic Speaker Verification Anti-Spoofing: Constant Q Cepstral Coefficients (2016, background): “A growing body of work has illustrated the vulnerability of ASV systems to a diverse range of spoofing attacks [1, 2].”
- ASVspoof 2015: the first automatic speaker verification spoofing and countermeasures challenge (2015, background): “Acknowledged vulnerabilities include attacks through impersonation, replay, speech synthesis and voice conversion [1].”
- Constant Q cepstral coefficients: A spoofing countermeasure for automatic speaker verification (2017, methodology): “The most potentially damaging spoofing attacks in this case are voice conversion and speech synthesis (Wu et al., 2015).”
- The ASVspoof 2017 Challenge: Assessing the Limits of Replay Spoofing Attack Detection (2017, background): “Automatic speaker verification (ASV) [1, 2, 3] technology is used in a growing range of applications which require not only robustness to changes in the acoustic environment, but also resilience to intentional circumvention, known as spoofing [4] or, according to ISO/IEC 30107-1:2016(1) standard, pr”
- ASVspoof 2021: Towards Spoofed and Deepfake Speech Detection in the Wild (2022, background): “have hence received greater attention [3].”
- One-Class Learning Towards Synthetic Voice Spoofing Detection (2020, background): “However, ASV systems can be fooled by spoofing attacks, such as impersonation (mimics or twins), replay (pre-recorded audio), text-to-speech (converting text to spoken words), and voice conversion (converting speech from source speaker to target speaker) [2], [3].”