Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Nair, Suja C.; S., Asha; K.V., Priya; Shaji, Kavya Clare P.; C., Balakrishnan; Kokilavani, T. Title A name given to the resource Tri-projection gated cross-modal fusion for robust multilingual emotion recognition Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Knowledge-Based Systems;Volume;344;Issue;;Article No.;116079; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.knosys.2026.116079" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.knosys.2026.116079</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037454999?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037454999?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Nair S.C., Department of Computer Science and Engineering, Muthoot Institute of Technology and Science, Kochi, India; S. A., Department of Computer Science and Engineering, Sahrdaya College of Engineering and Technology, Thrissur, India; K.V. P., Department of Computer Science and Engineering, School of Engineering and Sciences, SRM University-AP, Andhra Pradesh, Amaravati, 522240, India; Shaji K.C.P., Department of Computer Science and Engineering, Sahrdaya College of Engineering and Technology, Thrissur, India; C. B., Department of Computer Science, CHRIST University, Bengaluru, India; Kokilavani T., Department of Computer Science, CHRIST University, Bengaluru, India Description An account of the resource Existing multimodal approaches in emotion recognition (ER) rely on static or pairwise fusion strategies. These systems do not adequately address the challenges in real-world conversational systems, which require resilience to both multilingual code-switching and variable reliability of multiple modalities. We propose a transformer-based tri-modal emotion identification framework with a novel Tri-Projection Gated Cross-Modal Fusion (T-GCMF) module the first multimodal emotion recognition architecture explicitly designed for code-switched conversational input. T-GCMF simulates tri-modal interactions by explicitly calculating modality-specific confidence and cross-modal consistency, allowing for dynamic suppression of unreliable modalities during inference. Acoustic and visual cues are retrieved using CNNLSTM and deep CNN encoders, respectively. Textual representations are generated using XLM-RoBERTa to handle code-switched language reliably. We introduce Hinglish-MELD, the first multimodal emotion recognition dataset with aligned text, audio, and visual streams containing code-switched conversational content, filling a critical gap in the literature. With an accuracy of 88.3% and an F1-score of 87.0, the suggested confidence-aware fusion technique greatly surpasses unimodal, monolingual, and non-gated multimodal baselines. These findings demonstrate T-GCMF as a successful approach for emotion recognition in linguistically heterogeneous, real-world interactive systems and emphasize the significance of confidence-driven tri-modal integration. 2026 Subject The topic of the resource Affective computing; Code-switched emotion recognition; Confidence-aware multimodal fusion; Cross-modal modeling; Multilingual analysis Publisher An entity responsible for making the resource available Elsevier B.V. Relation A related resource ISSN: 9507051; CODEN: KNSYE Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online