Cornet Seminar – 31/01/2024

26 January 2024

The next seminar of the Cornet team will take place on January 31, 2024, at 11:35 am in S3 and will consist of two parts. Firstly, Felipe Albuquerque (LIA) will present his thesis topic on ‘The p-Median Problem with Coverage Constraints: New Resolution Methods and Application to the Design of Public Services.’ Following that, Luca Dini and Pierre Jourlin will present their ongoing work on the theme of ‘Hybrid Methods for Cognitive Attitudes Detection.’ Summary: In this seminar, we will present ongoing work on the transformation of a keyword spotting system into a concept-based labeling engine. We will highlight four major axes of this work:

SLG Seminar – Ryan Whetten – 01/02/2024

25 January 2024

The next SLG meeting will take place in room S5 on Thursday, February 1st, from 12:00 PM to 1:00 PM. Ryan Whetten will present his work, and you can find a brief introduction below. ——————————————————————— Open Implementation and Study of BEST-RQ for Speech Processing Abstract: Self-Supervised Learning (SSL) has proven to be useful in various speech tasks. However, these methods are generally very demanding in terms of data, memory, and computational resources. Recently, Google came out with a model called BEST-RQ (BERT-based Speech pre-Training with Random-projection Quantizer). Despite BEST-RQ’s great performance and simplicity, details are lacking in the original paper and there is no official easy-to-use open-source implementation. Furthermore, BEST-RQ has not been evaluated on other downstream tasks aside from ASR. In this presentation, we will discuss the details of my implementation of BEST-RQ and then see results from our preliminary study on four downstream tasks. Results show that a random projection quantizer can achieve similar downstream performance as wav2vec 2.0 while decreasing training time by over a factor of two.

SLG Seminar – Paul Gauthier Noé – 18/01/2024

10 January 2024

On 18 January from 12 am, we will host a talk from Dr. Paul Gauthier Noé on « Explaining probabilistic predictions … ». The presentation will be hosted on room S6.    More details will follow   Bio: Paul Gauthier Noe just received a PhD in Computer Science in Avignon Université under the supervision of Prof. Jean-François Bonastre and Dr. Driss Matrouf. He was working for the international JST-ANR VoicePersonae project and his main research interests are Speaker verification, Bayesian decision theory, Calibration of probabilities and Privacy in Speech.

PhD defense of Noé Cécillon – 18 January 2024

8 January 2024

Date: Thursday, January 18, 2023 at 14:00. Place: University of Avignon, Campus JH Fabre, Ada Lovelace amphitheater Jury : Title: Combining Graph and Text to Model Conversations: An Application to Online Abuse Detection. Abstract: Online abusive behaviors can have devastating consequences on individuals and communities. With the global expansion of internet and the social networks, anyone can be confronted with these behaviors. Over the past few years, laws and regulations have been established to regulate this kind of abuse but the responsibility ultimately lies with the platforms that host online communications. They are asked to monitor their users in order to prevent the proliferation of abusive content. Timely detection and moderation is a key factor to reduce the quantity and impact of abusive behaviors. However, due to the sheer quantity of online messages posted every day, platforms struggle to provide adequate resources. Since this implies high human and financial costs, companies have a keen interest in automating this process. Although it may seem a relatively simple task, it turns out to be quite complex. Indeed, malicious users have developed numerous techniques to bypass the standard automated methods. Allusions or implied meaning are other examples of strategies that automatic methods struggle Plus d'infos

SLG Seminar – Fenna Poletiek – 12/01/2024

8 January 2024

On 12 January from 12 am, we will host a virtual talk from Dr. Fenna Poletiek from Institute of Psychology at Leiden University on « Language learning in the lab ».   The presentation will be hosted on room S6.   Abstract: Language learning in the lab Language learning skills have been considered a defining feature of humanness. In this view language cannot be acquired by mere associative or statistical learning processes, only, like many other skills are learned by human and nonhuman primates during development. Indeed, the high (recursive) complexity of human grammars have been shown to make them impossible to learn by exposure to language exemplars only. Some research suggests, however, that at least some statistical learning is recruited in language acquisition (Perruchet & Pacton, 2006). And primates have been shown to mimic complex grammatical patterns after being trained on a sequence of stimulus responses (Rey et al., 2012). We performed series of studies with artificial languages in the lab, to investigate associative and statistical learning processes that support language learning. The results thus far suggest a fine tuned cooperation between three crucial features of the natural language learning process: first, learning proceeds ‘starting small’ with short simple sentences growing in complexity Plus d'infos