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*
*
Master 2 in Natural Language Processing, Speech Processing, computer
science or data science.
*
Good mastering of Python programming and deep learning framework.
*
Previous experience in bias in machine learning would be a plus
*
Very good communication skills in English
*
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 ([email protected]_
<mailto:[email protected]>), Solange
Rossato([email protected]) and François Portet
([email protected]_ <mailto:[email protected]>). 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).
--
François PORTET
Professeur - Univ Grenoble Alpes
Laboratoire d'Informatique de Grenoble - Équipe GETALP
Bâtiment IMAG - Office 333
700 avenue Centrale
Domaine Universitaire - 38401 St Martin d'Hères
FRANCE
Phone: +33 (0)4 57 42 15 44
Email:[email protected]
www:http://membres-liglab.imag.fr/portet/
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