PhD in ML/NLP – Fairness and self-supervised learning for speech processing Starting date: October 1st, 2023 (flexible) Application deadline: June 9th, 2023 Interviews (tentative): June 14th, 2023
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 societyrequires 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.
Both the question of bias and the concept of fairness have now become important aspects of AI, and we now have to find the right threshold between accuracy and the measure of fairness. Unfortunately, these notions of fairness and bias are challenging to define and their meanings can greatly differ [3].
The goals of this PhD position are threefold:
- 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*
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Master 2 in Natural Language Processing, Speech Processing, computer science or data science.
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Good mastering of Python programming and deep learning framework.
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Previous experience in bias in machine learning would be a plus
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Very good communication skills in English
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Good command of French would be a plus but is not mandatory
*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 providedboth 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_ mailto:mickael.rouvier@univ-avignon.fr), Solange Rossato(Solange.Rossato@imag.fr) and François Portet (_francois.Portet@imag.fr_ mailto: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_ 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 inProceedings of the 3rd Workshop on Gender Bias in Natural Language Processing (2021), 86–92. [3] Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K. & Galstyan, A. A Survey on Bias and Fairness in Machine Learning. ACMComput. Surv. 54. issn: 0360-0300. _https://doi.org/10.1145/3457607_ https://doi.org/10.1145/3457607(July 2021).