Summary of Ctc-based Non-autoregressive Textless Speech-to-speech Translation, by Qingkai Fang et al.
CTC-based Non-autoregressive Textless Speech-to-Speech Translation
by Qingkai Fang, Zhengrui Ma, Yan Zhou, Min Zhang, Yang Feng
First submitted to arxiv on: 11 Jun 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 A novel approach to direct speech-to-speech translation (S2ST) combines pretraining, knowledge distillation, and advanced non-autoregressive (NAR) training techniques to achieve high-quality translations at a significantly faster rate than traditional autoregressive (AR) models. By leveraging CTC-based NAR models, the proposed method achieves a 26.81 times speedup while matching the translation quality of AR models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Direct speech-to-speech translation can now be done quickly and accurately with a new approach that uses special techniques to train computer models. This makes it faster than previous methods, which took longer to complete. The same level of accuracy is achieved as before, but now in less time. |
Keywords
» Artificial intelligence » Autoregressive » Knowledge distillation » Pretraining » Translation
