Search Results for author: Jose Patino

Found 20 papers, 13 papers with code

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

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: 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

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

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

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

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

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

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

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