175 citations · 810 across the 46 of their papers we have counts for
51 papers
ASVspoof 5: Crowdsourced Speech Data, Deepfakes, and Adversarial Attacks at Scale
Xin Wang, Hector Delgado, Hemlata Tak +10
ASVspoof 5 is the fifth edition in a series of challenges that promote the study of speech spoofing and deepfake attacks, and the design of detection solutions. Compared to previou…
The VoicePrivacy 2022 Challenge: Progress and Perspectives in Voice Anonymisation
Michele Panariello, Natalia Tomashenko, Xin Wang +7
The VoicePrivacy Challenge promotes the development of voice anonymisation solutions for speech technology. In this paper we present a systematic overview and analysis of the secon…
The VoicePrivacy 2024 Challenge Evaluation Plan
Natalia Tomashenko, Xiaoxiao Miao, Pierre Champion +7
The task of the challenge is to develop a voice anonymization system for speech data which conceals the speaker's voice identity while protecting linguistic content and emotional s…
SynVox2: Towards a privacy-friendly VoxCeleb2 dataset
Xiaoxiao Miao, Xin Wang, Erica Cooper +5
The success of deep learning in speaker recognition relies heavily on the use of large datasets. However, the data-hungry nature of deep learning methods has already being question…
Speaker anonymization using neural audio codec language models
Michele Panariello, Francesco Nespoli, Massimiliano Todisco +1
The vast majority of approaches to speaker anonymization involve the extraction of fundamental frequency estimates, linguistic features and a speaker embedding which is perturbed t…
Spoofing attack augmentation: can differently-trained attack models improve generalisation?
Wanying Ge, Xin Wang, Junichi Yamagishi +2
A reliable deepfake detector or spoofing countermeasure (CM) should be robust in the face of unpredictable spoofing attacks. To encourage the learning of more generaliseable artefa…
Fairness and Privacy in Voice Biometrics:A Study of Gender Influences Using wav2vec 2.0
Oubaida Chouchane, Michele Panariello, Chiara Galdi +2
This study investigates the impact of gender information on utility, privacy, and fairness in voice biometric systems, guided by the General Data Protection Regulation (GDPR) manda…
Vocoder drift compensation by x-vector alignment in speaker anonymisation
Michele Panariello, Massimiliano Todisco, Nicholas Evans
For the most popular x-vector-based approaches to speaker anonymisation, the bulk of the anonymisation can stem from vocoding rather than from the core anonymisation function which…
Differentially Private Adversarial Auto-Encoder to Protect Gender in Voice Biometrics
Oubaïda Chouchane, Michele Panariello, Oualid Zari +4
Over the last decade, the use of Automatic Speaker Verification (ASV) systems has become increasingly widespread in response to the growing need for secure and efficient identity v…
Malafide: a novel adversarial convolutive noise attack against deepfake and spoofing detection systems
Michele Panariello, Wanying Ge, Hemlata Tak +2
We present Malafide, a universal adversarial attack against automatic speaker verification (ASV) spoofing countermeasures (CMs). By introducing convolutional noise using an optimis…
Vocoder drift in x-vector-based speaker anonymization
Michele Panariello, Massimiliano Todisco, Nicholas Evans
State-of-the-art approaches to speaker anonymization typically employ some form of perturbation function to conceal speaker information contained within an x-vector embedding, then…
Towards single integrated spoofing-aware speaker verification embeddings
Sung Hwan Mun, Hye-jin Shim, Hemlata Tak +12
This study aims to develop a single integrated spoofing-aware speaker verification (SASV) embeddings that satisfy two aspects. First, rejecting non-target speakers' input as well a…
Can spoofing countermeasure and speaker verification systems be jointly optimised?
Wanying Ge, Hemlata Tak, Massimiliano Todisco +1
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 verifi…
On the potential of jointly-optimised solutions to spoofing attack detection and automatic speaker verification
Wanying Ge, Hemlata Tak, Massimiliano Todisco +1
The spoofing-aware speaker verification (SASV) challenge was designed to promote the study of jointly-optimised solutions to accomplish the traditionally separately-optimised tasks…
ASVspoof 2021: Towards Spoofed and Deepfake Speech Detection in the Wild
Xuechen Liu, Xin Wang, Md Sahidullah +8
Benchmarking initiatives support the meaningful comparison of competing solutions to prominent problems in speech and language processing. Successive benchmarking evaluations typic…
The VoicePrivacy 2020 Challenge Evaluation Plan
Natalia Tomashenko, Brij Mohan Lal Srivastava, Xin Wang +8
The VoicePrivacy Challenge aims to promote the development of privacy preservation tools for speech technology by gathering a new community to define the tasks of interest and the…
Baseline Systems for the First Spoofing-Aware Speaker Verification Challenge: Score and Embedding Fusion
Hye-jin Shim, Hemlata Tak, Xuechen Liu +12
Deep learning has brought impressive progress in the study of both automatic speaker verification (ASV) and spoofing countermeasures (CM). Although solutions are mutually dependent…
The VoicePrivacy 2022 Challenge Evaluation Plan
Natalia Tomashenko, Xin Wang, Xiaoxiao Miao +7
For new participants - Executive summary: (1) The task is to develop a voice anonymization system for speech data which conceals the speaker's voice identity while protecting lingu…
Automatic speaker verification spoofing and deepfake detection using wav2vec 2.0 and data augmentation
Hemlata Tak, Massimiliano Todisco, Xin Wang +3
The performance of spoofing countermeasure systems depends fundamentally upon the use of sufficiently representative training data. With this usually being limited, current solutio…
Explainable deepfake and spoofing detection: an attack analysis using SHapley Additive exPlanations
Wanying Ge, Massimiliano Todisco, Nicholas Evans
Despite several years of research in deepfake and spoofing detection for automatic speaker verification, little is known about the artefacts that classifiers use to distinguish bet…
Exploring auditory acoustic features for the diagnosis of the Covid-19
Madhu R. Kamble, Jose Patino, Maria A. Zuluaga +1
The current outbreak of a coronavirus, has quickly escalated to become a serious global problem that has now been declared a Public Health Emergency of International Concern by the…
RawBoost: A Raw Data Boosting and Augmentation Method applied to Automatic Speaker Verification Anti-Spoofing
Hemlata Tak, Madhu Kamble, Jose Patino +2
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…
Explaining deep learning models for spoofing and deepfake detection with SHapley Additive exPlanations
Wanying Ge, Jose Patino, Massimiliano Todisco +1
Substantial progress in spoofing and deepfake detection has been made in recent years. Nonetheless, the community has yet to make notable inroads in providing an explanation for ho…
The VoicePrivacy 2020 Challenge: Results and findings
Natalia Tomashenko, Xin Wang, Emmanuel Vincent +11
This paper presents the results and analyses stemming from the first VoicePrivacy 2020 Challenge which focuses on developing anonymization solutions for speech technology. We provi…