1 code implementation • 21 Sep 2023 • Tomi Kinnunen, Kong Aik Lee, Hemlata Tak, Nicholas Evans, Andreas Nautsch
The proposed approach is a strong candidate metric for the tandem evaluation of PAD systems and biometric comparators.
1 code implementation • 13 Jun 2023 • Michele Panariello, Wanying Ge, Hemlata Tak, Massimiliano Todisco, Nicholas Evans
We present Malafide, a universal adversarial attack against automatic speaker verification (ASV) spoofing countermeasures (CMs).
1 code implementation • 30 May 2023 • Sung Hwan Mun, Hye-jin Shim, Hemlata Tak, Xin Wang, Xuechen Liu, Md Sahidullah, Myeonghun Jeong, Min Hyun Han, Massimiliano Todisco, Kong Aik Lee, Junichi Yamagishi, Nicholas Evans, Tomi Kinnunen, Nam Soo Kim, Jee-weon Jung
Second, competitive performance should be demonstrated compared to the fusion of automatic speaker verification (ASV) and countermeasure (CM) embeddings, which outperformed single embedding solutions by a large margin in the SASV2022 challenge.
1 code implementation • 13 Mar 2023 • Wanying Ge, Hemlata Tak, Massimiliano Todisco, Nicholas Evans
Spoofing countermeasure (CM) and automatic speaker verification (ASV) sub-systems can be used in tandem with a backend classifier as a solution to the spoofing aware speaker verification (SASV) task.
1 code implementation • 1 Sep 2022 • Wanying Ge, Hemlata Tak, Massimiliano Todisco, Nicholas Evans
The spoofing-aware speaker verification (SASV) challenge was designed to promote the study of jointly-optimised solutions to accomplish the traditionally separately-optimised tasks of spoofing detection and speaker verification.
no code implementations • 28 Mar 2022 • Jee-weon Jung, Hemlata Tak, Hye-jin Shim, Hee-Soo Heo, Bong-Jin Lee, Soo-Whan Chung, Ha-Jin Yu, Nicholas Evans, Tomi Kinnunen
Pre-trained spoofing detection and speaker verification models are provided as open source and are used in two baseline SASV solutions.
1 code implementation • 24 Feb 2022 • Hemlata Tak, Massimiliano Todisco, Xin Wang, Jee-weon Jung, Junichi Yamagishi, Nicholas Evans
The performance of spoofing countermeasure systems depends fundamentally upon the use of sufficiently representative training data.
1 code implementation • 8 Nov 2021 • Hemlata Tak, Madhu Kamble, Jose Patino, Massimiliano Todisco, Nicholas Evans
This paper introduces RawBoost, a data boosting and augmentation method for the design of more reliable spoofing detection solutions which operate directly upon raw waveform inputs.
1 code implementation • 4 Oct 2021 • Jee-weon Jung, Hee-Soo Heo, Hemlata Tak, Hye-jin Shim, Joon Son Chung, Bong-Jin Lee, Ha-Jin Yu, Nicholas Evans
Artefacts that differentiate spoofed from bona-fide utterances can reside in spectral or temporal domains.
Ranked #1 on Voice Anti-spoofing on ASVspoof 2019 - LA
1 code implementation • 27 Jul 2021 • Hemlata Tak, Jee-weon Jung, Jose Patino, Madhu Kamble, Massimiliano Todisco, Nicholas Evans
Artefacts that serve to distinguish bona fide speech from spoofed or deepfake speech are known to reside in specific subbands and temporal segments.
no code implementations • 8 Apr 2021 • Hemlata Tak, Jee-weon Jung, Jose Patino, Massimiliano Todisco, Nicholas Evans
This paper reports our use of graph attention networks (GATs) to model these relationships and to improve spoofing detection performance.
1 code implementation • 2 Nov 2020 • Hemlata Tak, Jose Patino, Massimiliano Todisco, Andreas Nautsch, Nicholas Evans, Anthony Larcher
Spoofing countermeasures aim to protect automatic speaker verification systems from attempts to manipulate their reliability with the use of spoofed speech signals.