The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
STC Antispoofing Systems for the ASVspoof2021 Challenge
Abstract
This paper presents the Speech Technology Center (STC) systems submitted to Automatic Speaker Verification Spoofing and Countermeasures (ASVspoof) Challenge 2015.In this work we investigate different acoustic feature spaces to determine reliable and robust countermeasures against spoofing attacks.In addition to the commonly used front-end MFCC features we explored features derived from phase spectrum and features based on applying the multiresolution wavelet transform.Similar to state-of-the-art ASV systems, we used the standard TV-JFA approach for probability modelling in spoofing detection systems.Experiments performed on the development and evaluation datasets of the Challenge demonstrate that the use of phase-related and wavelet-based features provides a substantial input into the efficiency of the resulting STC systems.In our research we also focused on the comparison of the linear (SVM) and nonlinear (DBN) classifiers. .
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“Thus, this demonstrates that the FIR-based strategy is not only useful to emulate telephonic or codec conditions [22], but also helps to train better antispoofing systems in challenging conditions.”
From The Vicomtech Audio Deepfake Detection System Based on Wav2vec2 for the 2022 ADD Challenge (2022) · cites this paper as [22]
In the citing paper
“This procedure has shown its success for ASVspoof 2021 LA and DF tracks [22] as it emulates the effects of transmission artifacts and codecs.”
From The Vicomtech Audio Deepfake Detection System Based on Wav2vec2 for the 2022 ADD Challenge (2022) · cites this paper as [22]
In the citing paper
“Moreover, they act by masking frequencies in the speech signal, improving the generalization capabilities of LA detection systems.”
From The Vicomtech Audio Deepfake Detection System Based on Wav2vec2 for the 2022 ADD Challenge (2022) · cites this paper as [22]
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Cites
A fast learning algorithm for deep belief nets
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A Wavelet Tour of Signal Processing
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Spoofing and countermeasures for speaker verification: A survey
Wu, Z., Evans, N., Kinnunen, T., Yamagishi, J., Alegre, F., & Li, H. (2015) · Speech Communication · doi:10.1016/j.specom.2014.10.005 · Cited by 601
ASVspoof 2015: the first automatic speaker verification spoofing and countermeasures challenge
Wu, Z., Kinnunen, T., Evans, N., Yamagishi, J., Hanilçi, C., Sahidullah, M., & Sizov, A. (2015) · Interspeech 2015 · doi:10.21437/interspeech.2015-462 · Cited by 484
LIBLINEAR: A Library for Large Linear Classification
FanRong-En, ChangKai-Wei, HsiehCho-Jui, WangXiang-Rui, & LinChih-Jen (2008) · Journal of Machine Learning Research · doi:10.5555/1390681.1442794 · Cited by 328
Dehak, N., Dehak, R., Kenny, P., Brümmer, N., Ouellet, P., & Dumouchel, P. (2009) · Interspeech 2009 · doi:10.21437/interspeech.2009-385 · Cited by 326
Detecting converted speech and natural speech for anti-spoofing attack in speaker recognition
Wu, Z., Chng, E. S., & Li, H. (2012) · Interspeech (USB) · doi:10.21437/interspeech.2012-465 · Cited by 176
ASVspoof 2021: Automatic Speaker Verification Spoofing and Countermeasures Challenge Evaluation Plan
Delgado, H., Evans, N., Kinnunen, T., Lee, K. A., Liu, X., Nautsch, A., Patino, J., Sahidullah, M., Todisco, M., Wang, X., et al. (2021) · arXiv · doi:10.48550/arxiv.2109.00535 · Cited by 129
STC Speaker Recognition System for the NIST i-Vector Challenge
Novoselov, S., Pekhovsky, T., & Simonchik, K. (2014) · The Speaker and Language Recognition Workshop (Odyssey) · doi:10.21437/odyssey.2014-35 · Cited by 25
Shchemelinin, V., Topchina, M., & Simonchik, K. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-11581-8_59 · Cited by 15
Improving Speech Synthesis Quality for Voices Created from an Audiobook Database
Геннадьевич, Ч. П., Zakharov, D., & Олегович, Т. А. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-11581-8_34 · Cited by 5
Speaker recognition system for the NIST SRE 2012
Kozlov, A., Kudashev, O., Matveev, Y. N., Pekhovsky, T., Simonchik, K., & Shulipa, A. (2014) · Informatics and Automation · doi:10.15622/sp.25.18 · Cited by 5
Cited by
ASVspoof 2021: Towards Spoofed and Deepfake Speech Detection in the Wild
Liu, X., Wang, X., Sahidullah, M., Patino, J., Delgado, H., Kinnunen, T., Todisco, M., Yamagishi, J., Evans, N., Nautsch, A., et al. (2023) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2023.3285283 · Cited by 312
Does Audio Deepfake Detection Generalize?
Müller, N., Czempin, P., Diekmann, F., Froghyar, A., & Böttinger, K. (2022) · Interspeech 2022 · doi:10.21437/interspeech.2022-108 · Cited by 258
A Survey on Speech Deepfake Detection
Li, M., Ahmadiadli, Y., & Zhang, X.-P. (2025) · ACM Computing Surveys · doi:10.1145/3714458 · Cited by 101
The Vicomtech Audio Deepfake Detection System Based on Wav2vec2 for the 2022 ADD Challenge
Martin-Donas, J. M., & Alvarez, A. (2022) · ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp43922.2022.9747768 · Cited by 100
A conformer-based classifier for variable-length utterance processing in anti-spoofing
Rosello, E., Gomez-Alanis, A., Gomez, A. M., & Peinado, A. (2023) · INTERSPEECH 2023 · doi:10.21437/interspeech.2023-1820 · Cited by 47
Metadata
- Authors
- Tomilov, A., Svishchev, A., Volkova, M., Chirkovskiy, A., Kondratev, A., & Lavrentyeva, G.
- ORCID
- Tomilov, Anton
- Venue
- 2021 Edition of the Automatic Speaker Verification and Spoofing Countermeasures Challenge
- Publisher
- ISCA
- Published
- 2021-09-16
- Pages
- 61-67
- OpenAlex
- W3199161700
- Cited by
- 71
- References
- 12
- Citations by year
- 2026: 4 · 2025: 14 · 2024: 11 · 2023: 18 · 2022: 24
- FWCI
- 6.12
- Type
- conference paper
- Language
- en
- Open access
- closed
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Biometric Identification and Security
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Keywords
- speaker verification, anti-spoofing, multiresolution wavelet transform, Mel-frequency cepstral coefficients, support vector machine, deep belief network
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