AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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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.
This work proposes a data-efficient space alignment method based on Heterogeneous Graph Neural Networks and link prediction that yields results comparable to those of SAMU-XLSR, a state-of-the-art method, and even surpasses it for the Yemba language in the task of word retrieval, while using far fewer resources.
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