Author

Marie Tahon

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#natural language process... Preprint Sep 2026

Enhancing speech representation learning with cross-modal knowledge transfer with HGNN under low resource settings: the case study of Yemba

This work proposes to enhance acoustic representation trough a cross-modal transfer knowledge approach, based on heterogeneous graph neural networks (HGNNs), where acoustic and linguistic entities are modeled as distinct node types within a unified graph.

Y. Y. Nzeuhang, Paulin Melatagia Yonta, Marie Tahon · 0 citations

#natural language process... Preprint Sep 2026

Low resource cross-modal alignment using HGNN to enhance speech representation

Speech-text space alignment is a multimodal representation learning method consisting to map different speech and text into a shared representation space, leading to enrichment of the representation of each modality. Proposed architectures, such as SAMU-XLSR, typically follow a student/teacher framework, with the goal...

Y. Y. Nzeuhang, Marie Tahon, Paulin Melatagia Yonta · 0 citations