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CORDIS - EU research results
CORDIS

Transforming European Industrial Ecosystems through eXtended Reality enhanced with human-centric AI and secure, 5G-enabled IoT

CORDIS provides links to public deliverables and publications of HORIZON projects.

Links to deliverables and publications from FP7 projects, as well as links to some specific result types such as dataset and software, are dynamically retrieved from OpenAIRE .

Deliverables

State of the art analysis for XR and related enabling technologies (opens in new window)

In this deliverable, a thorough assessment of the state of the art and existing results coming from related research initiatives will take place.

Publications

Enrolment-based personalisation for improving individual-level fairness in speech emotion recognition (opens in new window)

Author(s): Andreas Triantafyllopoulos; Björn W. Schuller
Published in: Interspeech 2024, 2024
DOI: 10.48550/ARXIV.2406.06665

INTERSPEECH 2009 Emotion Challenge Revisited: Benchmarking 15 Years of Progress in Speech Emotion Recognition (opens in new window)

Author(s): Andreas Triantafyllopoulos; Anton Batliner; Simon David Noel Rampp; Manuel Milling; Björn W. Schuller
Published in: Interspeech 2024, 2024
Publisher: ISCA
DOI: 10.21437/INTERSPEECH.2024-97

Time-Series Forecasting in Industrial Environments: A Performance Study and a Novel Late Fusion Framework (opens in new window)

Author(s): Dimitrios Oikonomou, Lampros Leontaris, Nikolaos Dimitriou, Dimitrios Tzovaras
Published in: IEEE Sensors Journal, Issue 25, 2025, ISSN 1530-437X
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
DOI: 10.1109/JSEN.2025.3526362

Overcoming occlusions in AR, via multi-view, real-time 3D human pose estimation (opens in new window)

Author(s): Ioannis Pastaltzidis, Iason Karakostas, Nikolaos Dimitriou, Stelios Krinidis, Dimitrios Tzovaras
Published in: Machine Vision and Applications, Issue 37, 2026, ISSN 0932-8092
Publisher: Springer Science and Business Media LLC
DOI: 10.1007/S00138-025-01783-9

Audio Enhancement for Computer Audition—An Iterative Training Paradigm Using Sample Importance (opens in new window)

Author(s): Milling, Manuel; Liu, Shuo; Triantafyllopoulos, Andreas; Aslan, Ilhan; Schuller, Björn W.
Published in: Journal of Computer Science and Technology, 2024, ISSN 1860-4749
Publisher: Springer
DOI: 10.48550/ARXIV.2408.06264

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