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Grenoble INP - UGA

corporate_fareorganization
Web page: http://www.grenoble-inp.fr/welcome/
Country: France

ROR: https://ror.org/05sbt2524

ISNI: 0000000417654326

Wikidata: Q1665121

Grenoble INP - UGA

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618 Projects, page 1 of 124

  • OSCODI (Osteo-inductive coating of orthopedic and dental implants)

    assignment_turned_inProject2013 - 2014Partners:Grenoble INP - UGA
    Funder: European Commission Project Code: 334966
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  • BIOMIM (Biomimetic films and membranes as advanced materials for studies on cellular processes)

    assignment_turned_inProject2011 - 2016Partners:Grenoble INP - UGA
    Funder: European Commission Project Code: 259370
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  • UNITE

    assignment_turned_inProjectFrom 2019Partners:Grenoble INP - UGA
    Funder: French National Research Agency (ANR) Project Code: ANR-19-GURE-0015
    Funder Contribution: 590,000 EUR
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  • ProsoDeep (Deep understanding and modelling of the hierarchical structure of Prosody)

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Open Access Mandate for Publications and Research data assignment_turned_inProject2017 - 2019Partners:Grenoble INP - UGA
    Funder: European Commission Project Code: 745802
    Overall Budget: 123,384 EURFunder Contribution: 123,384 EUR

    The prime objective of the ProsoDeep project is to gain a deeper understanding of the language of prosody through the analysis of all the levels in the production hierarchy of prosody. In particular, it will exploit the benefits of the top-down and bottom-up approaches through their incorporation within a Deep Prosody Model (DPM). The DPM will facilitate the advancement of speech technologies that rely both on the synthesis of prosody, e.g. text-to-speech (TTS) systems, and its analysis, e.g. speech emotion recognition (SER). To reach this objective the project will draw on a variety of scientific fields, including signal processing, physiology of prosody production and biomechanics, linguistics, and machine learning, and will also be augmented with respiratory measurements.

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  • MLTSRMSRFID (Machine Learning-Based Test Solutions for Reliable Mixed-Signal/RF Integrated Devices)

    assignment_turned_inProject2007 - 2011Partners:Grenoble INP - UGA
    Funder: European Commission Project Code: 209653
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