Summary of Can We Achieve High-quality Direct Speech-to-speech Translation Without Parallel Speech Data?, by Qingkai Fang et al.
Can We Achieve High-quality Direct Speech-to-Speech Translation without Parallel Speech Data?
by Qingkai Fang, Shaolei Zhang, Zhengrui Ma, 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 The proposed two-pass direct speech-to-speech translation (S2ST) models decompose the task into speech-to-text translation (S2TT) and text-to-speech (TTS), yielding promising results. However, training these models still relies on parallel speech data, which is challenging to collect. The authors introduce a composite S2ST model named ComSpeech that integrates any pretrained S2TT and TTS models into a direct S2ST model. To eliminate reliance on parallel speech data, they propose a novel training method ComSpeech-ZS that utilizes only S2TT and TTS data through contrastive learning. This enables the synthesis capability learned from TTS to generalize to S2ST in a zero-shot manner. Experimental results show that when parallel speech data is available, ComSpeech surpasses previous two-pass models like UnitY and Translatotron 2 in both translation quality and decoding speed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to translate speech directly from one language to another without needing any extra training data. They create a special kind of model that combines the best parts of existing speech-to-text and text-to-speech models. This allows them to train their model using only text and speech data, rather than requiring parallel speech data like previous models did. The results show that their new method is better than previous methods when there’s enough training data, but it’s still not as good as the best existing methods when there isn’t any extra data. |
Keywords
» Artificial intelligence » Translation » Zero shot
