Summary of Retrieval Augmented End-to-end Spoken Dialog Models, by Mingqiu Wang et al.
Retrieval Augmented End-to-End Spoken Dialog Models
by Mingqiu Wang, Izhak Shafran, Hagen Soltau, Wei Han, Yuan Cao, Dian Yu, Laurent El Shafey
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 In this research paper, the authors introduce SLM, a novel joint speech and language model that combines a pre-trained foundational speech model with a large language model (LLM). The proposed approach fuses these models while retaining the in-context learning capabilities of the LLM. This fusion enables SLM to infer dialog states directly from audio signals, demonstrating its effectiveness in speech dialog applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a new type of AI model that can understand and process both spoken words and written language. It combines two existing models: one that’s good at recognizing spoken words and another that’s great at understanding written text. This combined model is called SLM, or “joint speech and language model.” The authors show how this model can be used to figure out what someone is trying to say just by listening to their voice. |
Keywords
» Artificial intelligence » Language model » Large language model
