Cornet Seminar – Felipe Albuquerque – 13/01/2023

13 January 2023

In the context of team Cornet’s seminars, Felipe Albuquerque (LIA/Espace) will present his research work on January 13, 2023, at 11:35 in the meeting room. Abstract: Frequently, social network information has been used to solve applications in Operation Research, such as the Team Formation Problem, whose goal is to find a subset of the workers that collectively cover a set of skills and can communicate effectively with each other. We use the Structural Balance Theory to define the compatibility between pairs of workers in the same team. For such, the social networks are represented by signed graphs, and the compatibility metric is calculated from the analysis of possible positive paths between pairs of distinct vertices. To solve this new version of the problem, we introduce an Integer Linear Programming formulation and a decomposition for it. We present an analysis of the performed computational tests that prove the potential efficiency of the decomposition proposed.

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

ANR BRUEL Project

1 January 2023

Development of a methodology for evaluating voice identification systems The BRUEL project concerns the evaluation/certification of voice identification systems against adversarial attacks. Indeed, speaker recognition systems are vulnerable not only to speech artificially produced by voice synthesis but also to other forms of attacks such as voice identity conversion and replay attacks. The artifacts created during the creation or manipulation of these fraudulent attacks leave marks in the signal by voice synthesis algorithms, thus distinguishing the original real voice from a forged voice. Under these conditions, detecting identity theft requires evaluating identity theft countermeasures concurrently with speaker recognition systems. The BRUEL project aims to propose the first methodology for evaluating/certifying voice identification systems based on a Common Criteria approach. List of partners: CEA Eurecom Service National de Police Scientifique IRCAM LIA Laboratoire d’Informatique d’Avignon Project Coordinator: LIA Scientific Manager for LIA: Driss Matrouf Start Date: 01/01/2023 End Date: 30/06/2026 More

ANR EVA Project

1 January 2023

Explicit Voice Attributes Describing a voice in a few words remains a very arbitrary task. We can speak with a “deep”, “breathy”, “bright” or “hoarse” voice, but the full characterization of a voice would require a close set of rigorously defined attributes constituting an ontology. However, such a description grid does not exist. Machine learning applied to speech also suffers the same weakness : in most automatic processing tasks, when a speaker is modeled, abstract global representations are used without making their characteristics explicit. For instance, automatic speaker verification / identification is usually tackled thanks to the x-vectors paradigm, which consists in describing a speaker’s voice by an embedding vector only designed to distinguish speakers. Despite their very good accuracy for speaker identification, x-vectors are usually unsuitable to detect similarities between different voices with common characteristics. The same observations can be made for speech generation. We propose to carry out a comprehensive set of analyses to extract salient, unaddressed voice attributes to enrich structured representations usable for synthesis and voice conversion. Partner list: Project leader: Orange Scientific leader for LIA: Yannick Estève Start date: 01/01/2023 — End date: 31/12/2025 More