Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : LAMSADE (Paris) / LIG (Grenoble)
Durée : 3 ans
Contact : alexandre.allauzen@dauphine.psl.eu
Date limite de publication : 2022-09-20

Contexte :
The ANR project E-SSL (Efficient Self-Supervised Learning for
Inclusive and Innovative Speech Technologies) will start on November
1st. 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 results of
various systems.

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.

Sujet :

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
theirmeanings 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. 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.

To apply, send a CV and a cover letter to A. Allauzen before September
the 12th

Profil du candidat :
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 Self-Supervised Learning, acoustic modeling
or ASR would be a plus
– Very good communication skills in English
– Good command of French would be a plus but is not mandatory

Formation et compétences requises :
See skills.

Adresse d’emploi :
Université Paris-Dauphine / Laboratoire d’Informatique de Grenoble