Summary of A Non-autoregressive Generation Framework For End-to-end Simultaneous Speech-to-speech Translation, by Zhengrui Ma et al.
A Non-autoregressive Generation Framework for End-to-End Simultaneous Speech-to-Speech Translation
by Zhengrui Ma, Qingkai Fang, Shaolei Zhang, Shoutao Guo, Yang Feng, Min Zhang
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 This paper proposes a novel non-autoregressive generation framework for simultaneous speech translation (NAST-S2X), which integrates speech-to-text and speech-to-speech tasks into a unified end-to-end framework. NAST-S2X uses a non-autoregressive decoder that can concurrently generate multiple text or acoustic unit tokens upon receiving fixed-length speech chunks, allowing it to dynamically adjust its latency using CTC decoding. The paper shows that NAST-S2X outperforms state-of-the-art models in both speech-to-text and speech-to-speech tasks, achieving high-quality simultaneous interpretation within a delay of less than 3 seconds and providing a 28 times decoding speedup in offline generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to translate speech into text and speech into speech at the same time. Right now, most translation systems are just one-way – they can only translate from one language to another. But sometimes we need to translate in both directions, like when two people are talking and want to understand each other immediately. The old way of doing this was to break it down into smaller steps, which didn’t work well because the errors added up. This new method is different because it does all the translation at once, without breaking it down into smaller parts. It works really well and can even predict what someone will say next, so you don’t have to wait for them to finish talking. |
Keywords
» Artificial intelligence » Autoregressive » Decoder » Translation
