Summary of Wav2graph: a Framework For Supervised Learning Knowledge Graph From Speech, by Khai Le-duc et al.
wav2graph: A Framework for Supervised Learning Knowledge Graph from Speech
by Khai Le-Duc, Quy-Anh Dang, Tan-Hanh Pham, Truong-Son Hy
First submitted to arxiv on: 8 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel framework, wav2graph, is introduced for constructing knowledge graphs (KGs) from speech data, enhancing the performance of large language models and search engines. The pipeline involves transcribing spoken utterances, creating a KG, converting it into embedding vectors, and training graph neural networks (GNNs) for node classification and link prediction tasks. Extensive experiments are conducted in inductive and transductive learning contexts using state-of-the-art GNN models on human transcripts and automatic speech recognition (ASR) transcripts, evaluating both encoder-based and decoder-based node embeddings, as well as monolingual and multilingual acoustic pre-trained models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to make computers understand spoken words is developed. It’s called wav2graph. This tool helps big language models and search engines work better by using a special kind of data called knowledge graphs. These graphs are usually made from text, but this tool can also use spoken words. The process starts with transcribing what people say, then making the graph, converting it into numbers that computers can understand, and finally training the computer to make predictions about what’s being said. The results show how well this works in different situations and with different types of speech recognition. |
Keywords
* Artificial intelligence * Classification * Decoder * Embedding * Encoder * Gnn
