ANR TRADEF Project

1 January 2023

Tracking and Detecting Fake News and Deepfakes in Arabic Social Networks The 4th Generation War (4GW) is known as an information war involving non-military populations. It is conducted by national or transnational groups following ideologies based on cultural beliefs, religion, economic or political interests, aiming to sow chaos in a targeted region globally. In 1989, authors discussing the 4th generation war, some of whom were military experts, explained that it would be widespread and challenging to define in the decades to come. With the emergence of social networks, the previously vague battlefield found a platform for 4GW. One of its penetration points is the extensive use of social networks to manipulate opinions, aiming to shape the targeted region’s perspective to accept a certain state of affairs and render it socially and politically acceptable. Much like the 4th generation war, cognitive warfare aims to blur comprehension mechanisms regarding politics, economy, religion, etc. Its consequence is destabilizing and weakening the adversary. This cognitive war targets what is assumed to be the enemy’s brain, altering reality by flooding the adversary’s population with misleading information, rumors, or manipulated videos. Furthermore, the proliferation of social bots today enables automated dissemination of disinformation on social networks. Plus d'infos

ANR ESSL Project

1 January 2023

Efficient Self-Supervised Learning for Inclusive and Innovative Speech Technologies Self-Supervised Learning (SSL) has recently emerged as an incredibly promising artificial intelligence (AI) method. Through this method, massive amounts of unlabeled data that are accessible can be utilized by AI systems to surpass known performances. Particularly, the field of Automatic Speech Processing (ASP) is swiftly being transformed by the arrival of SSL, thanks in part to massive industrial investments and the explosion of data, both provided by a handful of companies. The performance gains are impressive, but the complexity of SSL models requires researchers and industry professionals in the field to have extraordinary computational capacity, drastically limiting access to fundamental research in this area and its deployment in everyday products. For instance, a significant portion of work using an SSL model for ASP relies on a system maintained and provided by a single company (wav2vec 2.0). The entire lifecycle of the technology, from its theoretical foundations to its practical deployment and societal analysis, therefore depends solely on institutions with the physical and financial means to support the intensity of this technique’s development. The E-SSL project aims to restore to the scientific community and ASP industry the necessary control over self-supervised learning Plus d'infos

H2020 SELMA Project

1 January 2021

Stream Learning for Multilingual Knowledge Transfer The internet contains vast amounts of data and information in various languages, both written and audiovisual. There’s an increasing need to leverage this largely untapped resource. The SELMA project, funded by the EU, focuses on ingesting and monitoring large quantities of data. It systematically trains machine learning models to perform tasks in natural language and utilizes these models to monitor data streams, aiming to enhance multilingual media monitoring and real-time content production. Ultimately, the project will advance cutting-edge techniques in language modeling, automatic translation, speech recognition, and synthesis. Project Coordinator: Deutsche Welle, DE Scientific Lead for LIA: Yannick ESTEVE Start Date: 01/01/2021 End Date: 30/12/2023 More

ANR DEEP-PRIVACY Project

1 January 2019

Distributed, Personalized, Privacy-Preserving Learning for Speech Processing The project focuses on developing distributed, personalized, and privacy-preserving approaches for speech recognition. We propose an approach where each user’s device locally performs private computations and does not share raw voice data, while certain inter-user computations (such as model enrichment) are conducted on a server or a peer-to-peer network, with voice data shared after anonymization. Objectives: Speech recognition is now used in numerous applications, including virtual assistants that collect, process, and store personal voice data on centralized servers, raising serious privacy concerns. The use of embedded speech recognition addresses these privacy aspects, but only during the speech recognition phase. However, there is still a need to further improve speech recognition technology as its performance remains limited in adverse conditions (e.g., noisy environments, reverberant speech, strong accents, etc.). This can only be achieved from large speech corpora representative of real and diverse usage conditions. Hence, there is a necessity to share voice data while ensuring privacy. Improvements obtained through shared voice data will then benefit all users. <br /><br />In this context, DEEP-PRIVACY proposes a new paradigm based on a distributed, personalized, and privacy-preserving approach. Some processing occurs on the user’s terminal, ensuring privacy Plus d'infos