  Knowledge graphs (KGs) enhance the performance of large language models
(LLMs) and search engines by providing structured, interconnected data that
improves reasoning and context-awareness. However, KGs only focus on text data,
thereby neglecting other modalities such as speech. In this work, we introduce
wav2graph, the first framework for supervised learning knowledge graph from
speech data. Our pipeline are straightforward: (1) constructing a KG based on
transcribed spoken utterances and a named entity database, (2) converting KG
into embedding vectors, and (3) training graph neural networks (GNNs) for node
classification and link prediction tasks. Through extensive experiments
conducted in inductive and transductive learning contexts using
state-of-the-art GNN models, we provide baseline results and error analysis for
node classification and link prediction tasks on human transcripts and
automatic speech recognition (ASR) transcripts, including evaluations using
both encoder-based and decoder-based node embeddings, as well as monolingual
and multilingual acoustic pre-trained models. All related code, data, and
models are published online.
