*PhD fellowship*
*
*
/
/Computational model of conversational behaviors integrating behavioral
and physiological data from human-human and human-machine interactions/
/
/
/
/Laboratoire d’Informatique et des Systèmes (LIS) et Laboratoire Parole
et Langage (LPL) /
Aix-Marseille Université & CNRS
*_Keywords:_* conversational speech, multimodal data analysis,
neurophysiological data, machine learning, artificial agents.
The PhD project is part of an A*MIDEX project /PhysSocial/ that aims at
a betterunderstanding of the specificities of social interactions by
comparing relationships between behavior and neurophysiologyin
human‐human and human‐robot discussion. The goal of the PhD is toanalyze
the multimodal signals (speech, eyes direction, physiological, and
neurophysiologic signals) from conversational activity using signal
processing and machine learning methodologies in order to compare the
human-human and human-robot interactions.
The PhD is organized around 3 main tasks:
* /Multimodal data preprocessing/: in a first step, the objective is
to process the row data (speech, transcribed speech, eyes tracking,
physiological and neurophysiological signals) corresponding to
human-human and human-robot conversation in order to extract time
series corresponding to behavioral features, as well as cognitive
events derived from local activity in well-defined brain areas
involved inlanguage and social cognition
* /Machine learning of causal relations: /in a second step,//time
series will be used by statistical learning to identify causal
relations between behavioral and physiological features and
cognitive events extracted from neurophysiological recording with
fMRI. From a learning point of view, one challenge in this project
is the high-dimensional data. We address this issue with a focus on
the features representation and selection problems.
* /Computational modeling and implementation in a humanoid robot/: the
last step consists in integrating the knowledge extracted from the
data sets into an existing platform (Furhat talking head) in order
to generate the appropriate behavior (speech and eyes behavior) of
the artificial agent during an interaction with an user. The model
will be evaluated through a experimentation that will be conducted
in collaboration with the other partners of the project.
The PhD candidate should have a master's degree completed in Computer
Science, Applied Mathematics, Signal or Natural Language Processing
(with solid background in machine learning).
The candidate should have a strong background in machine learning and
signal processing with a focus on multimodality. Some complementary
previous experience would be appreciated in the following topics:
• Multimodal data processing
• Data science applied to language data
• Dialogue systems
The PhD is fully funded during 3 years as part of the A*MIDEX
interdisciplinary project PhysSocial, including personalized training,
travel expenses, and conferences attendance.
French language is not required.
Aix Marseille University (http://www.univ-amu.fr/en), the largest French
University, is ideally located on the Mediterranean coast, and only 1h30
away from the Alps.
The application files consists of the following documents:
- A detailed curriculum,
- A description of the academic background and copy of academic records
and most recent diploma,
- A cover letter describing why the applicant wishes to participate in
this project, a justification of the inter-disciplinary of her/his
research, his/her training project and career Plan including these
dimensions, and his/her research’s adequacy with the proposed topics
- 2 recommendation letters (including one from the master or equivalent
diploma supervisor)
The application files should be sent to :
Laurent Prévot: [log in to unmask]
<mailto:[log in to unmask]>
and
Magalie Ochs: [log in to unmask] <mailto:[log in to unmask]>
For any question, contact :
Laurent Prévot: [log in to unmask]
<mailto:[log in to unmask]>
www.lpl-aix.fr/person/*prevot* <http://www.lpl-aix.fr/person/*prevot*>
Magalie Ochs: [log in to unmask] <mailto:[log in to unmask]>
http://www.lsis.org/ochsm/
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