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.
no code implementations • 13 Feb 2024 • Théo Mariotte, Anthony Larcher, Silvio Montrésor, Jean-Hugh Thomas
A channel-number invariant loss is proposed to learn a unique feature representation regardless of the number of available microphones.
no code implementations • 24 Jul 2023 • Martin Lebourdais, Théo Mariotte, Marie Tahon, Anthony Larcher, Antoine Laurent, Silvio Montresor, Sylvain Meignier, Jean-Hugh Thomas
Voice activity and overlapped speech detection (respectively VAD and OSD) are key pre-processing tasks for speaker diarization.
no code implementations • 7 Jun 2023 • Théo Mariotte, Anthony Larcher, Silvio Montrésor, Jean-Hugh Thomas
Pipeline systems rely on speech segmentation to extract speakers' segments and achieve robust speaker diarization.
no code implementations • 15 Apr 2023 • Hubert Nourtel, Pierre Champion, Denis Jouvet, Anthony Larcher, Marie Tahon
This paper studies the impact of the speaker anonymization baseline system of the VPC on emotional information present in speech utterances.
no code implementations • 22 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 Automatic Speech Recognition (ASR) +4
no code implementations • 15 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).
1 code implementation • 8 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.
no code implementations • 24 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.
no code implementations • 21 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.
1 code implementation • 2 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.
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.
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.
no code implementations • 16 Apr 2019 • Kong Aik Lee, Ville Hautamaki, Tomi Kinnunen, Hitoshi Yamamoto, Koji Okabe, Ville Vestman, Jing Huang, Guohong Ding, Hanwu Sun, Anthony Larcher, Rohan Kumar Das, Haizhou Li, Mickael Rouvier, Pierre-Michel Bousquet, Wei Rao, Qing Wang, Chunlei Zhang, Fahimeh Bahmaninezhad, Hector Delgado, Jose Patino, Qiongqiong Wang, Ling Guo, Takafumi Koshinaka, Jiacen Zhang, Koichi Shinoda, Trung Ngo Trong, Md Sahidullah, Fan Lu, Yun Tang, Ming Tu, Kah Kuan Teh, Huy Dat Tran, Kuruvachan K. George, Ivan Kukanov, Florent Desnous, Jichen Yang, Emre Yilmaz, Longting Xu, Jean-Francois Bonastre, Cheng-Lin Xu, Zhi Hao Lim, Eng Siong Chng, Shivesh Ranjan, John H. L. Hansen, Massimiliano Todisco, Nicholas Evans
The I4U consortium was established to facilitate a joint entry to NIST speaker recognition evaluations (SRE).
no code implementations • JEPTALNRECITAL 2016 • Ga{\"e}l Le Lan, Sylvain Meignier, Delphine Charlet, Anthony Larcher
This paper investigates self trained cross-show speaker diarization applied to collections of French TV archives, based on an \textit{i-vector/PLDA} framework.
no code implementations • JEPTALNRECITAL 2016 • Natalia Tomashenko, Yuri Khokhlov, Anthony Larcher, Yannick Est{\`e}ve
L{'}{\'e}tude pr{\'e}sent{\'e}e dans cet article am{\'e}liore une m{\'e}thode r{\'e}cemment propos{\'e}e pour l{'}adaptation de mod{\`e}les acoustiques markoviens coupl{\'e}s {\`a} un r{\'e}seau de neurones profond (DNN-HMM).
no code implementations • 5 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).
no code implementations • JEPTALNRECITAL 2012 • Anthony Larcher, Pierre-Michel Bousquet, Driss Matrouf, Jean-Francois Bonastre