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ISCApad Archive » 2023 » ISCApad #305 » Jobs » (2023-06-04) PhD in ML/NLP @ Dauphine Université PSL, Paris and Université Grenoble Alpes, France |
ISCApad #305 |
Friday, November 10, 2023 by Chris Wellekens |
PhD in ML/NLP – Fairness and self-supervised learning for speech processing Salary: ~2000€ gross/month (social security included) Mission: research oriented (teaching possible but not mandatory)
Keywords: speech processing, fairness, bias, self-supervised learning, evaluation metrics
CONTEXT This thesis is in the context of the ANR project E-SSL (Efficient Self-Supervised Learning for Inclusive and Innovative Speech Technologies). Self-supervised learning (SSL) has recently emerged as one of the most promising artificial intelligence (AI) methods as it becomes now feasible to take advantage of the colossal amounts of existing unlabeled data to significantly improve the performances of various speech processing tasks.
PROJECT OBJECTIVES Speech technologies are widely used in our daily life and are expanding the scope of our action, with decision-making systems, including in critical areas such as health or legal aspects. In these societal applications, the question of the use of these tools raises the issue of the possible discrimination of people according to criteria for which society requires equal treatment, such as gender, origin, religion or disability... Recently, the machine learning community has been confronted with the need to work on the possible biases of algorithms, and many works have shown that the search for the best performance is not the only goal to pursue [1]. For instance, recent evaluations of ASR systems have shown that performances can vary according to the gender but these variations depend both on data used for learning and on models [2]. Therefore such systems are increasingly scrutinized for being biased while trustworthy speech technologies definitely represents a crucial expectation.
- First make a survey on the many definitions of robustness, fairness and bias with the aim of coming up with definitions and metrics fit for speech SSL models - Then gather speech datasets with high amount of well-described metadata - Setup an evaluation protocol for SSL models and analyzing the results.
SKILLS
SCIENTIFIC ENVIRONMENT The PhD position will be co-supervised by Alexandre Allauzen (Dauphine Université PSL, Paris) and Solange Rossato and François Portet (Université Grenoble Alpes). Joint meetings are planned on a regular basis and the student is expected to spend time in both places. Moreover, two other PhD positions are open in this project. The students, along with the partners will closely collaborate. For instance, specific SSL models along with evaluation criteria will be developed by the other PhD students. Moreover, the PhD student will collaborate with several team members involved in the project in particular the two other PhD candidates who will be recruited and the partners from LIA, LIG and Dauphine Université PSL, Paris. The means to carry out the PhD will be provided both in terms of missions in France and abroad and in terms of equipment. The candidate will have access to the cluster of GPUs of both the LIG and Dauphine Université PSL. Furthermore, access to the National supercomputer Jean-Zay will enable to run large scale experiments.
INSTRUCTIONS FOR APPLYING Applications must contain: CV + letter/message of motivation + master notes + be ready to provide letter(s) of recommendation; and be addressed to Alexandre Allauzen (alexandre.allauzen@espci.psl.eu), Solange Rossato (Solange.Rossato@imag.fr) and François Portet (francois.Portet@imag.fr). We celebrate diversity and are committed to creating an inclusive environment for all employees.
REFERENCES: [1] Mengesha, Z., Heldreth, C., Lahav, M., Sublewski, J. & Tuennerman, E. “I don’t Think These Devices are Very Culturally Sensitive.”—Impact of Automated Speech Recognition Errors on African Americans. Frontiers in Artificial Intelligence 4. issn: 2624-8212. https://www.frontiersin.org/article/10.3389/frai.2021.725911 (2021). [2] Garnerin, M., Rossato, S. & Besacier, L. Investigating the Impact of Gender Representation in ASR Training Data: a Case Study on Librispeech in Proceedings of the 3rd Workshop on Gender Bias in Natural Language Processing (2021), 86–92. |
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