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Found 39 papers, 21 papers with code
Can spoofing countermeasure and speaker verification systems be jointly optimised?
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.
On the potential of jointly-optimised solutions to spoofing attack detection and automatic speaker verification
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.
The VoicePrivacy 2020 Challenge Evaluation Plan
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 evaluation methodology, and benchmarking solutions through a series of challenges.
The PartialSpoof Database and Countermeasures for the Detection of Short Fake Speech Segments Embedded in an Utterance
Since the short spoofed speech segments to be embedded by attackers are of variable length, six different temporal resolutions are considered, ranging from as short as 20 ms to as large as 640 ms. Third, we propose a new CM that enables the simultaneous use of the segment-level labels at different temporal resolutions as well as utterance-level labels to execute utterance- and segment-level detection at the same time.
SASV 2022: The First Spoofing-Aware Speaker Verification Challenge
Pre-trained spoofing detection and speaker verification models are provided as open source and are used in two baseline SASV solutions.
The VoicePrivacy 2022 Challenge Evaluation Plan
Participants apply their developed anonymization systems, run evaluation scripts and submit objective evaluation results and anonymized speech data to the organizers.
Explainable deepfake and spoofing detection: an attack analysis using SHapley Additive exPlanations
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 between bona fide and spoofed utterances.
Automatic speaker verification spoofing and deepfake detection using wav2vec 2.0 and data augmentation
The performance of spoofing countermeasure systems depends fundamentally upon the use of sufficiently representative training data.
RawBoost: A Raw Data Boosting and Augmentation Method applied to Automatic Speaker Verification Anti-Spoofing
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
Substantial progress in spoofing and deepfake detection has been made in recent years.
AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks
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
ASVspoof 2021: accelerating progress in spoofed and deepfake speech detection
In addition to a continued focus upon logical and physical access tasks in which there are a number of advances compared to previous editions, ASVspoof 2021 introduces a new task involving deepfake speech detection.
ASVspoof 2021: Automatic Speaker Verification Spoofing and Countermeasures Challenge Evaluation Plan
The automatic speaker verification spoofing and countermeasures (ASVspoof) challenge series is a community-led initiative which aims to promote the consideration of spoofing and the development of countermeasures.
The VoicePrivacy 2020 Challenge: Results and findings
We provide a systematic overview of the challenge design with an analysis of submitted systems and evaluation results.
End-to-End Spectro-Temporal Graph Attention Networks for Speaker Verification Anti-Spoofing and Speech Deepfake Detection
Artefacts that serve to distinguish bona fide speech from spoofed or deepfake speech are known to reside in specific subbands and temporal segments.
Raw Differentiable Architecture Search for Speech Deepfake and Spoofing Detection
End-to-end approaches to anti-spoofing, especially those which operate directly upon the raw signal, are starting to be competitive with their more traditional counterparts.
Visualizing Classifier Adjacency Relations: A Case Study in Speaker Verification and Voice Anti-Spoofing
Whether it be for results summarization, or the analysis of classifier fusion, some means to compare different classifiers can often provide illuminating insight into their behaviour, (dis)similarity or complementarity.
Graph Attention Networks for Anti-Spoofing
This paper reports our use of graph attention networks (GATs) to model these relationships and to improve spoofing detection performance.
Partially-Connected Differentiable Architecture Search for Deepfake and Spoofing Detection
This paper reports the first successful application of a differentiable architecture search (DARTS) approach to the deepfake and spoofing detection problems.
An Initial Investigation for Detecting Partially Spoofed Audio
By definition, partially-spoofed utterances contain a mix of both spoofed and bona fide segments, which will likely degrade the performance of countermeasures trained with entirely spoofed utterances.
Biometrics in the Era of COVID-19: Challenges and Opportunities
Since early 2020 the COVID-19 pandemic has had a considerable impact on many aspects of daily life.
ASVspoof 2019: spoofing countermeasures for the detection of synthesized, converted and replayed speech
The ASVspoof initiative was conceived to spearhead research in anti-spoofing for automatic speaker verification (ASV).
Modelling Verbal Morphology in Nen
Nen verbal morphology is remarkably complex; a transitive verb can take up to 1, 740 unique forms.
Speaker anonymisation using the McAdams coefficient
Anonymisation has the goal of manipulating speech signals in order to degrade the reliability of automatic approaches to speaker recognition, while preserving other aspects of speech, such as those relating to intelligibility and naturalness.
End-to-end anti-spoofing with RawNet2
Spoofing countermeasures aim to protect automatic speaker verification systems from attempts to manipulate their reliability with the use of spoofed speech signals.
Texture-based Presentation Attack Detection for Automatic Speaker Verification
Despite these and other advantages, biometric systems in general and Automatic speaker verification (ASV) systems in particular can be vulnerable to attack presentations.
Speech Pseudonymisation Assessment Using Voice Similarity Matrices
The proliferation of speech technologies and rising privacy legislation calls for the development of privacy preservation solutions for speech applications.
Tandem Assessment of Spoofing Countermeasures and Automatic Speaker Verification: Fundamentals
Recent years have seen growing efforts to develop spoofing countermeasures (CMs) to protect automatic speaker verification (ASV) systems from being deceived by manipulated or artificial inputs.
To compress or not to compress? A Finite-State approach to Nen verbal morphology
While the {`}Chunking{'} model is under half the size of the full de-composed counterpart, the decomposition displays higher structural order.
The Privacy ZEBRA: Zero Evidence Biometric Recognition Assessment
Mounting privacy legislation calls for the preservation of privacy in speech technology, though solutions are gravely lacking.
Cryptography and Security Audio and Speech Processing
Introducing the VoicePrivacy Initiative
The VoicePrivacy initiative 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 evaluation methodology, and benchmarking solutions through a series of challenges.
Self-Supervised Feature Extraction for 3D Axon Segmentation
Existing learning-based methods to automatically trace axons in 3D brain imagery often rely on manually annotated segmentation labels.
The Speed Submission to DIHARD II: Contributions & Lessons Learned
This paper describes the speaker diarization systems developed for the Second DIHARD Speech Diarization Challenge (DIHARD II) by the Speed team.
ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech
Spoofing attacks within a logical access (LA) scenario are generated with the latest speech synthesis and voice conversion technologies, including state-of-the-art neural acoustic and waveform model techniques.
Speaker Anonymization Using X-vector and Neural Waveform Models
One solution to mitigate these concerns involves the concealing of speaker identities before the sharing of speech data.
I4U Submission to NIST SRE 2018: Leveraging from a Decade of Shared Experiences
The I4U consortium was established to facilitate a joint entry to NIST speaker recognition evaluations (SRE).
Introduction to Voice Presentation Attack Detection and Recent Advances
Over the past few years significant progress has been made in the field of presentation attack detection (PAD) for automatic speaker recognition (ASV).
The EURECOM Submission to the First DIHARD Challenge
The first DIHARD challenge aims to promote speaker diarization research and to foster progress in domain robustness.
t-DCF: a Detection Cost Function for the Tandem Assessment of Spoofing Countermeasures and Automatic Speaker Verification
The two challenge editions in 2015 and 2017 involved the assessment of spoofing countermeasures (CMs) in isolation from ASV using an equal error rate (EER) metric.
