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

Excerpt

arXiv:2609.23194v1 Announce Type: new Abstract: Acoustic representation learning is crucial for speech processing, yet low-resource languages (LRLs) face severe data scarcity, limiting the effectiveness of traditional and self-supervised methods. As a promising alternative, in this work, we propose 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 distinc