Search Results for author: Anthony Larcher

Found 14 papers, 2 papers with code

Overlaps and Gender Analysis in the Context of Broadcast Media

no code implementations LREC 2022 Martin Lebourdais, Marie Tahon, Antoine Laurent, Sylvain Meignier, Anthony Larcher

Our main goal is to study the interactions between speakers according to their gender and role in broadcast media.

Are disentangled representations all you need to build speaker anonymization systems?

no code implementations22 Aug 2022 Pierre Champion, Denis Jouvet, Anthony Larcher

We propose enhancing the disentanglement by removing speaker information from the acoustic model using vector quantization.

Automatic Speech Recognition Disentanglement +3

Privacy-Preserving Speech Representation Learning using Vector Quantization

no code implementations15 Mar 2022 Pierre Champion, Denis Jouvet, Anthony Larcher

With the popularity of virtual assistants (e. g., Siri, Alexa), the use of speech recognition is now becoming more and more widespread. However, speech signals contain a lot of sensitive information, such as the speaker's identity, which raises privacy concerns. The presented experiments show that the representations extracted by the deep layers of speech recognition networks contain speaker information. This paper aims to produce an anonymous representation while preserving speech recognition performance. To this end, we propose to use vector quantization to constrain the representation space and induce the network to suppress the speaker identity. The choice of the quantization dictionary size allows to configure the trade-off between utility (speech recognition) and privacy (speaker identity concealment).

Privacy Preserving Quantization +3

On the invertibility of a voice privacy system using embedding alignement

1 code implementation8 Oct 2021 Pierre Champion, Thomas Thebaud, Gaël Le Lan, Anthony Larcher, Denis Jouvet

This paper explores various attack scenarios on a voice anonymization system using embeddings alignment techniques.

Translation

Evaluating X-vector-based Speaker Anonymization under White-box Assessment

no code implementations24 Sep 2021 Pierre Champion, Denis Jouvet, Anthony Larcher

In the scenario of the Voice Privacy challenge, anonymization is achieved by converting all utterances from a source speaker to match the same target identity; this identity being randomly selected.

A Study of F0 Modification for X-Vector Based Speech Pseudonymization Across Gender

no code implementations21 Jan 2021 Pierre Champion, Denis Jouvet, Anthony Larcher

Speech pseudonymization aims at altering a speech signal to map the identifiable personal characteristics of a given speaker to another identity.

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

\'Evaluation de syst\`emes apprenant tout au long de la vie (Evaluation of lifelong learning systems )

no code implementations JEPTALNRECITAL 2020 Yevhenii Prokopalo, Sylvain Meignier, Olivier Galibert, Lo{\"\i}c Barrault, Anthony Larcher

Une adaptation de leur mod{\`e}le par des experts en apprentissage automatique est possible mais tr{\`e}s co{\^u}teuse alors que les soci{\'e}t{\'e}s utilisant ces syst{\`e}mes disposent d{'}experts du domaine qui pourraient accompagner ces syst{\`e}mes dans un apprentissage tout au long de la vie.

Evaluation of Lifelong Learning Systems

no code implementations LREC 2020 Yevhenii Prokopalo, Sylvain Meignier, Olivier Galibert, Loic Barrault, Anthony Larcher

Current intelligent systems need the expensive support of machine learning experts to sustain their performance level when used on a daily basis.

BIG-bench Machine Learning

Fantastic 4 system for NIST 2015 Language Recognition Evaluation

no code implementations5 Feb 2016 Kong Aik Lee, Ville Hautamäki, Anthony Larcher, Wei Rao, Hanwu Sun, Trung Hieu Nguyen, Guangsen Wang, Aleksandr Sizov, Ivan Kukanov, Amir Poorjam, Trung Ngo Trong, Xiong Xiao, Cheng-Lin Xu, Hai-Hua Xu, Bin Ma, Haizhou Li, Sylvain Meignier

This article describes the systems jointly submitted by Institute for Infocomm (I$^2$R), the Laboratoire d'Informatique de l'Universit\'e du Maine (LIUM), Nanyang Technology University (NTU) and the University of Eastern Finland (UEF) for 2015 NIST Language Recognition Evaluation (LRE).

regression

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