Search Results for author: Nicholas Evans

Found 54 papers, 32 papers with code

Spoof Diarization: "What Spoofed When" in Partially Spoofed Audio

1 code implementation12 Jun 2024 Lin Zhang, Xin Wang, Erica Cooper, Mireia Diez, Federico Landini, Nicholas Evans, Junichi Yamagishi

As a pioneering study in spoof diarization, we focus on defining the task, establishing evaluation metrics, and proposing a benchmark model, namely the Countermeasure-Condition Clustering (3C) model.

Clustering

To what extent can ASV systems naturally defend against spoofing attacks?

no code implementations8 Jun 2024 Jee-weon Jung, Xin Wang, Nicholas Evans, Shinji Watanabe, Hye-jin Shim, Hemlata Tak, Sidhhant Arora, Junichi Yamagishi, Joon Son Chung

The current automatic speaker verification (ASV) task involves making binary decisions on two types of trials: target and non-target.

Speaker Verification

Harder or Different? Understanding Generalization of Audio Deepfake Detection

no code implementations5 Jun 2024 Nicolas M. Müller, Nicholas Evans, Hemlata Tak, Philip Sperl, Konstantin Böttinger

Recent research has highlighted a key issue in speech deepfake detection: models trained on one set of deepfakes perform poorly on others.

Audio Deepfake Detection DeepFake Detection +1

A Comparison of Differential Performance Metrics for the Evaluation of Automatic Speaker Verification Fairness

no code implementations27 Apr 2024 Oubaida Chouchane, Christoph Busch, Chiara Galdi, Nicholas Evans, Massimiliano Todisco

When decisions are made and when personal data is treated by automated processes, there is an expectation of fairness -- that members of different demographic groups receive equitable treatment.

Face Recognition Fairness +1

The VoicePrivacy 2024 Challenge Evaluation Plan

1 code implementation3 Apr 2024 Natalia Tomashenko, Xiaoxiao Miao, Pierre Champion, Sarina Meyer, Xin Wang, Emmanuel Vincent, Michele Panariello, Nicholas Evans, Junichi Yamagishi, Massimiliano Todisco

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 states.

Speaker anonymization using neural audio codec language models

2 code implementations25 Sep 2023 Michele Panariello, Francesco Nespoli, Massimiliano Todisco, Nicholas Evans

The vast majority of approaches to speaker anonymization involve the extraction of fundamental frequency estimates, linguistic features and a speaker embedding which is perturbed to obfuscate the speaker identity before an anonymized speech waveform is resynthesized using a vocoder.

Language Modelling

t-EER: Parameter-Free Tandem Evaluation of Countermeasures and Biometric Comparators

1 code implementation21 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.

Fairness and Privacy in Voice Biometrics:A Study of Gender Influences Using wav2vec 2.0

no code implementations27 Aug 2023 Oubaida Chouchane, Michele Panariello, Chiara Galdi, Massimiliano Todisco, Nicholas Evans

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) mandates, which underscore the need for minimizing the processing and storage of private and sensitive data, and ensuring fairness in automated decision-making systems.

Decision Making Fairness +1

Vocoder drift compensation by x-vector alignment in speaker anonymisation

no code implementations17 Jul 2023 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 is used to substitute an original speaker x-vector with that of a fictitious pseudo-speaker.

Vocoder drift in x-vector-based speaker anonymization

1 code implementation5 Jun 2023 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 resynthesize utterances in the voice of a new pseudo-speaker using a vocoder.

Towards single integrated spoofing-aware speaker verification embeddings

1 code implementation30 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.

Speaker Verification

Range-Based Equal Error Rate for Spoof Localization

1 code implementation28 May 2023 Lin Zhang, Xin Wang, Erica Cooper, Nicholas Evans, Junichi Yamagishi

To properly measure misclassified ranges and better evaluate spoof localization performance, we upgrade point-based EER to range-based EER.

Can spoofing countermeasure and speaker verification systems be jointly optimised?

1 code implementation13 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.

Speaker Verification

On the potential of jointly-optimised solutions to spoofing attack detection and automatic speaker verification

1 code implementation1 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.

Speaker Verification

The VoicePrivacy 2020 Challenge Evaluation Plan

1 code implementation14 May 2022 Natalia Tomashenko, Brij Mohan Lal Srivastava, Xin Wang, Emmanuel Vincent, Andreas Nautsch, Junichi Yamagishi, Nicholas Evans, Jose Patino, Jean-François Bonastre, Paul-Gauthier Noé, Massimiliano Todisco

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.

Benchmarking

The PartialSpoof Database and Countermeasures for the Detection of Short Fake Speech Segments Embedded in an Utterance

no code implementations11 Apr 2022 Lin Zhang, Xin Wang, Erica Cooper, Nicholas Evans, Junichi Yamagishi

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.

Speaker Verification Speech Synthesis +2

SASV 2022: The First Spoofing-Aware Speaker Verification Challenge

no code implementations28 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.

Speaker Verification

The VoicePrivacy 2022 Challenge Evaluation Plan

2 code implementations23 Mar 2022 Natalia Tomashenko, Xin Wang, Xiaoxiao Miao, Hubert Nourtel, Pierre Champion, Massimiliano Todisco, Emmanuel Vincent, Nicholas Evans, Junichi Yamagishi, Jean-François Bonastre

Participants apply their developed anonymization systems, run evaluation scripts and submit objective evaluation results and anonymized speech data to the organizers.

Speaker Verification

Explainable deepfake and spoofing detection: an attack analysis using SHapley Additive exPlanations

1 code implementation28 Feb 2022 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 between bona fide and spoofed utterances.

Face Swapping Speaker Verification

RawBoost: A Raw Data Boosting and Augmentation Method applied to Automatic Speaker Verification Anti-Spoofing

1 code implementation8 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.

Speaker Verification Voice Anti-spoofing

ASVspoof 2021: Automatic Speaker Verification Spoofing and Countermeasures Challenge Evaluation Plan

1 code implementation1 Sep 2021 Héctor Delgado, Nicholas Evans, Tomi Kinnunen, Kong Aik Lee, Xuechen Liu, Andreas Nautsch, Jose Patino, Md Sahidullah, Massimiliano Todisco, Xin Wang, Junichi Yamagishi

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.

Face Swapping Speaker Verification

ASVspoof 2021: accelerating progress in spoofed and deepfake speech detection

no code implementations1 Sep 2021 Junichi Yamagishi, Xin Wang, Massimiliano Todisco, Md Sahidullah, Jose Patino, Andreas Nautsch, Xuechen Liu, Kong Aik Lee, Tomi Kinnunen, Nicholas Evans, Héctor Delgado

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.

Face Swapping Speaker Verification

Raw Differentiable Architecture Search for Speech Deepfake and Spoofing Detection

1 code implementation26 Jul 2021 Wanying Ge, Jose Patino, Massimiliano Todisco, Nicholas Evans

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.

Face Swapping

Visualizing Classifier Adjacency Relations: A Case Study in Speaker Verification and Voice Anti-Spoofing

1 code implementation11 Jun 2021 Tomi Kinnunen, Andreas Nautsch, Md Sahidullah, Nicholas Evans, Xin Wang, Massimiliano Todisco, Héctor Delgado, Junichi Yamagishi, Kong Aik Lee

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.

Speaker Verification Voice Anti-spoofing

Graph Attention Networks for Anti-Spoofing

no code implementations8 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.

Graph Attention Speaker Verification

Partially-Connected Differentiable Architecture Search for Deepfake and Spoofing Detection

1 code implementation7 Apr 2021 Wanying Ge, Michele Panariello, Jose Patino, Massimiliano Todisco, Nicholas Evans

This paper reports the first successful application of a differentiable architecture search (DARTS) approach to the deepfake and spoofing detection problems.

Face Swapping Neural Architecture Search

An Initial Investigation for Detecting Partially Spoofed Audio

no code implementations6 Apr 2021 Lin Zhang, Xin Wang, Erica Cooper, Junichi Yamagishi, Jose Patino, Nicholas Evans

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.

Voice Anti-spoofing

Modelling Verbal Morphology in Nen

no code implementations ALTA 2020 Saliha Muradoğlu, Nicholas Evans, Ekaterina Vylomova

Nen verbal morphology is remarkably complex; a transitive verb can take up to 1, 740 unique forms.

End-to-end anti-spoofing with RawNet2

1 code implementation2 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.

Speaker Verification

Speaker anonymisation using the McAdams coefficient

2 code implementations2 Nov 2020 Jose Patino, Natalia Tomashenko, Massimiliano Todisco, Andreas Nautsch, Nicholas Evans

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.

Speaker Recognition

Texture-based Presentation Attack Detection for Automatic Speaker Verification

no code implementations8 Oct 2020 Lazaro J. Gonzalez-Soler, Jose Patino, Marta Gomez-Barrero, Massimiliano Todisco, Christoph Busch, Nicholas Evans

Despite these and other advantages, biometric systems in general and Automatic speaker verification (ASV) systems in particular can be vulnerable to attack presentations.

Speaker Verification

Speech Pseudonymisation Assessment Using Voice Similarity Matrices

2 code implementations30 Aug 2020 Paul-Gauthier Noé, Jean-François Bonastre, Driss Matrouf, Natalia Tomashenko, Andreas Nautsch, Nicholas Evans

The proliferation of speech technologies and rising privacy legislation calls for the development of privacy preservation solutions for speech applications.

De-identification Voice Similarity

Tandem Assessment of Spoofing Countermeasures and Automatic Speaker Verification: Fundamentals

no code implementations12 Jul 2020 Tomi Kinnunen, Héctor Delgado, Nicholas Evans, Kong Aik Lee, Ville Vestman, Andreas Nautsch, Massimiliano Todisco, Xin Wang, Md Sahidullah, Junichi Yamagishi, Douglas A. Reynolds

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.

Speaker Verification

To compress or not to compress? A Finite-State approach to Nen verbal morphology

no code implementations ACL 2020 Saliha Muradoglu, Nicholas Evans, Hanna Suominen

While the {`}Chunking{'} model is under half the size of the full de-composed counterpart, the decomposition displays higher structural order.

Chunking

The Privacy ZEBRA: Zero Evidence Biometric Recognition Assessment

2 code implementations19 May 2020 Andreas Nautsch, Jose Patino, Natalia Tomashenko, Junichi Yamagishi, Paul-Gauthier Noe, Jean-Francois Bonastre, Massimiliano Todisco, Nicholas Evans

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

3 code implementations4 May 2020 Natalia Tomashenko, Brij Mohan Lal Srivastava, Xin Wang, Emmanuel Vincent, Andreas Nautsch, Junichi Yamagishi, Nicholas Evans, Jose Patino, Jean-François Bonastre, Paul-Gauthier Noé, Massimiliano Todisco

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.

Benchmarking

Self-Supervised Feature Extraction for 3D Axon Segmentation

1 code implementation20 Apr 2020 Tzofi Klinghoffer, Peter Morales, Young-Gyun Park, Nicholas Evans, Kwanghun Chung, Laura J. Brattain

Existing learning-based methods to automatically trace axons in 3D brain imagery often rely on manually annotated segmentation labels.

Segmentation

Introduction to Voice Presentation Attack Detection and Recent Advances

no code implementations4 Jan 2019 Md Sahidullah, Hector Delgado, Massimiliano Todisco, Tomi Kinnunen, Nicholas Evans, Junichi Yamagishi, Kong-Aik Lee

Over the past few years significant progress has been made in the field of presentation attack detection (PAD) for automatic speaker recognition (ASV).

Benchmarking Speaker Recognition

The EURECOM Submission to the First DIHARD Challenge

1 code implementation6 Sep 2018 Jose Patino, Héctor Delgado, Nicholas Evans

The first DIHARD challenge aims to promote speaker diarization research and to foster progress in domain robustness.

Clustering speaker-diarization +1

t-DCF: a Detection Cost Function for the Tandem Assessment of Spoofing Countermeasures and Automatic Speaker Verification

1 code implementation25 Apr 2018 Tomi Kinnunen, Kong Aik Lee, Hector Delgado, Nicholas Evans, Massimiliano Todisco, Md Sahidullah, Junichi Yamagishi, Douglas A. Reynolds

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.

Speaker Verification

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