A5066811192- Speech Recognition and Synthesis
- Speech and Audio Processing
- Music and Audio Processing
- Natural Language Processing Techniques
- Advanced Adaptive Filtering Techniques
- Hate Speech and Cyberbullying Detection
- Digital Media Forensic Detection
- Speech and dialogue systems
- Advanced Data Compression Techniques
- Biometric Identification and Security
- Voice and Speech Disorders
- Adversarial Robustness in Machine Learning
- Gait Recognition and Analysis
- Blind Source Separation Techniques
- Face recognition and analysis
- Acoustic Wave Phenomena Research
- Anomaly Detection Techniques and Applications
- Structural Health Monitoring Techniques
- Topic Modeling
- Wireless Signal Modulation Classification
- Music Technology and Sound Studies
- Face and Expression Recognition
- Digital Rights Management and Security
- Hearing Loss and Rehabilitation
- Advanced Malware Detection Techniques
EURECOM
2015-2024
Royal Free London NHS Foundation Trust
2023-2024
University of Cambridge
2020-2023
The King's College
2023
King's College Hospital
2023
Oak Ridge National Laboratory
2022
Addenbrooke's Hospital
2021
The University of Sydney
2007-2019
Sydney Hospital
2019
Royal Prince Alfred Hospital
2019
The ASVspoof initiative was created to promote the development of countermeasures which aim protect automatic speaker verification (ASV) from spoofing attacks.The first community-led, common evaluation held in 2015 focused on for speech synthesis and voice conversion attacks.Arguably, however, it is replay attacks pose greatest threat.Such involve recordings collected enrolled speakers order provoke false alarms can be mounted with greater ease using everyday consumer devices.ASVspoof 2017,...
An increasing number of independent studies have confirmed the vulnerability automatic speaker verification (ASV) technology to spoofing.However, in comparison that involving other biometric modalities, spoofing and countermeasure research for ASV is still its infancy.A current barrier progress lack standards which impedes results generated by different researchers.The ASVspoof initiative aims overcome this bottleneck through provision standard corpora, protocols metrics support a common...
ASVspoof 2021 is the forth edition in series of bi-annual challenges which aim to promote study spoofing and design countermeasures protect automatic speaker verification systems from manipulation. In addition a continued focus upon logical physical access tasks there are number advances compared previous editions, introduces new task involving deepfake speech detection. This paper describes all three tasks, databases for each them, evaluation metrics, four challenge baselines, platform...
ASVspoof, now in its third edition, is a series of community-led challenges which promote the development countermeasures to protect automatic speaker verification (ASV) from threat spoofing. Advances 2019 edition include: (i) consideration both logical access (LA) and physical (PA) scenarios three major forms spoofing attack, namely synthetic, converted replayed speech; (ii) attacks generated with state-of-the-art neural acoustic waveform models; (iii) an improved, controlled simulation...
Spoofing countermeasures aim to protect automatic speaker verification systems from being manipulated by spoofed speech signals. While results the most recent ASVspoof 2019 evaluation show great potential detect forms of attack, some continue evade detection. This paper reports first application RawNet2 anti-spoofing. ingests raw audio and has learn cues that are not detectable using more traditional countermeasure solutions. We describe modifications made original architecture so it can be...
Concerns regarding the vulnerability of automatic speaker verification (ASV) technology against spoofing can undermine confidence in its reliability and form a barrier to exploitation. The absence competitive evaluations lack common datasets has hampered progress developing effective countermeasures. This paper describes ASV Spoofing Countermeasures (ASVspoof) initiative, which aims fill this void. Through provision dataset, protocols, metrics, ASVspoof promotes sound research methodology...
The now-acknowledged vulnerabilities of automatic speaker verification (ASV) technology to spoofing attacks have spawned interests develop so-called countermeasures.By providing common databases, protocols and metrics for their assessment, the ASVspoof initiative was born spearhead research in this area.The first competitive challenge held 2015 focused on assessment countermeasures protect ASV from voice conversion speech synthesis attacks.The second switched focus consideration replay...
The ASVspoof initiative was conceived to spearhead research in anti-spoofing for automatic speaker verification (ASV). This paper describes the third a series of bi-annual challenges: 2019. With challenge database and protocols being described elsewhere, focus this is on results top performing single ensemble system submissions from 62 teams, all which out-perform two baseline systems, often by substantial margin. Deeper analyses shows that performance dominated specific conditions involving...
Artefacts that serve to distinguish bona fide speech from spoofed or deepfake are known reside in specific subbands and temporal segments. Various approaches can be used capture model such artefacts, however, none works well across a spectrum of diverse spoofing attacks. Reliable detection then often depends upon the fusion multiple systems, each tuned detect different forms attack. In this paper we show better performance achieved when is performed within itself representation learned...
Benchmarking initiatives support the meaningful comparison of competing solutions to prominent problems in speech and language processing. Successive benchmarking evaluations typically reflect a progressive evolution from ideal lab conditions towards those encountered wild. ASVspoof, spoofing deepfake detection initiative challenge series, has followed same trend. This article provides summary ASVspoof 2021 results 54 participating teams that submitted evaluation phase. For logical access...
It is widely acknowledged that most biometric systems are vulnerable to spoofing, also known as imposture.While vulnerabilities and countermeasures for other modalities have been studied, e.g.face verification, speaker verification remain vulnerable.This paper describes some specific studied in the literature presents a brief survey of recent work develop spoofing countermeasures.The concludes with discussion on need standard datasets, metrics formal evaluations which needed assess realistic...
Speech recordings are a rich source of personal, sensitive data that can be used to support plethora diverse applications, from health profiling biometric recognition. It is therefore essential speech adequately protected so they cannot misused. Such protection, in the form privacy-preserving technologies, required ensure that: (i) profiles given individual (e.g., across different service operators) unlinkable; (ii) leaked, encrypted information irreversible, and (iii) references renewable....
This paper describes a new database for the assessment of automatic speaker verification (ASV) vulnerabilities to spoofing attacks. In contrast other recent data collection efforts, has been designed support development replay countermeasures tailored towards protection text-dependent ASV systems from attacks in face variable recording and playback conditions. Derived re-recording original RedDots database, effort is aligned with that thus well positioned future assessments countermeasures,...
The automatic speaker verification spoofing and countermeasures (ASVspoof) challenge series is a community-led initiative which aims to promote the consideration of development countermeasures. ASVspoof 2021 4th in bi-annual, competitive challenges where goal develop capable discriminating between bona fide spoofed or deepfake speech. This document provides technical description challenge, including details training, evaluation data, metrics, baselines, rules, submission procedures schedule.
The social media revolution has produced a plethora of web services to which users can easily upload and share multimedia documents.Despite the popularity convenience such services, sharing inherently personal data, including speech raises obvious security privacy concerns.In particular, user's data may be acquired used with synthesis systems produce high-quality utterances reflect same speaker identity.These then attack verification systems.One solution mitigate these concerns involves...
The performance of spoofing countermeasure systems depends fundamentally upon the use sufficiently representative training data. With this usually being limited, current solutions typically lack generalisation to attacks encountered in wild. Strategies improve reliability face uncontrolled, unpredictable are hence needed. We report paper our efforts self-supervised learning form a wav2vec 2.0 front-end with fine tuning. Despite initial base representations learned using only bona fide data...
Since early 2020, the COVID-19 pandemic has had a considerable impact on many aspects of daily life. A range different measures have been implemented worldwide to reduce rate new infections and manage pressure national health services. primary strategy gatherings potential for transmission through prioritisation remote working education. Enhanced hand hygiene use facial masks decreased spread pathogens when are unavoidable. These particular present challenges reliable biometric recognition,...