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