The CRISSP research team carries out fundamental and applied research in the field of automatic speech processing and social robotics.
In particular, we aim to :
- capture, analyze and model the various verbal and co-verbal signals involved in a communicative interaction situation.
- enhance the socio-communicative capabilities of humanoid robots
- develop voice technologies that exploit the multimodal characteristics of speech (sound, vision, gestures), in particular to help people with disabilities (voice substitution, speech rehabilitation systems, communication aids for the hearing-impaired, reading aids).
- to better understand, through modeling and simulation, some of the processes involved in speech and language acquisition, perception and control.
The main research themes of the CRISSP team are :
- Text-based speech synthesis, with a focus on expressivity, reactivity (incremental TTS synthesis), prosody modeling, audiovisual synthesis (avatar) and gesture control.
- Human-robot interaction: analysis, modeling and generation of verbal and co-verbal signals (e.g. gaze, head movements)
- Acoustic-articulatory modeling (inversion, synthesis, silent speech interface, biofeedback)
- Automatic processing of gesture-based language, with a focus on Cued-speech.
The team is involved in 3 chairs of the Grenoble-based 3IA artificial intelligence institute MIAI.

Chiffres-clés
7
permanents
6
doctoral & post-doctoral students
Axes de recherche
- in construction
Keywords
speech processing, speech production, HRI, prosody, self supervised learning, audio source separation, sound localization, cued speech, artificial intelligence
Platforms and experimentation
CRISSP exploits several experimental platforms to acquire the multimodal signals that are implied in speech production and interaction (articulation, head movements, gaze, ...):
Projects
Theradia
2019-2025

Financement : BPI France
Coordinateur :
SilentPitch
2023-2027
Financement : ANR-23-CE33-0016
Coordinateur :
Serveur gestuel
Financement : BPI France
Coordinateur :
Fluence
Trans3
2017-2024

Financement :
Coordinateur :
Faits marquants
2021
Publication de l'article "Dynamical Variational Autoencoders: A Comprehensive Review" dans Foundations and Trends in Machine Learning (Vol. 15, No. 1-2, pp 1-175). Cet article a été réalisé en collaboration avec INRIA et LPNC dans le cadre de l'institut 3IA MIAI et a déjà cité plus de 250 fois.
Pascal PERRIER et Laurent GIRIN sont membres de la chaire Bayesian Cognition and Machine Learning for Speech communication de l'Institut MIAI Grenoble Alpes.
Partners
Academic partners
- LPNC
- LIG
- INSERM (Grenoble)
- ENS, LISN (Paris)
- LIS (Marseille)
- Centrale Superlec
- Irisa (Rennes)
- LPP (Paris)
- Institut Jean le Rond d'Alembert (Paris)
- University of Edinburgh
- University College London
Industrial partners
- Orange
- ATOS
- NaverLabs
- Arturia
- Vogo
- Orange
- Ives
- Dynamic.XYZ
- Humans Matter