Summary of Diffusion Synthesizer For Efficient Multilingual Speech to Speech Translation, by Nameer Hirschkind et al.
Diffusion Synthesizer for Efficient Multilingual Speech to Speech Translation
by Nameer Hirschkind, Xiao Yu, Mahesh Kumar Nandwana, Joseph Liu, Eloi DuBois, Dao Le, Nicolas Thiebaut, Colin Sinclair, Kyle Spence, Charles Shang, Zoe Abrams, Morgan McGuire
First submitted to arxiv on: 14 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 The researchers introduce DiffuseST, a direct speech-to-speech translation system that can translate multiple source languages into English while preserving the input speaker’s voice zero-shot. The system utilizes a diffusion-based synthesizer component, which outperforms a Tacotron-based synthesizer in terms of audio quality metrics such as MOS and PESQ, as well as speaker similarity. While having more parameters, the diffusion synthesizer achieves lower latency, enabling the entire model to run faster than real-time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DiffuseST is a new way to translate speech from one language to another while keeping the same voice. The system can do this without needing to learn about specific voices beforehand. The team tested two different ways of making the translated speech sound like the original, and found that their new method outperformed the old one in several areas. |
Keywords
* Artificial intelligence * Diffusion * Translation * Zero shot
