Summary of Streamspeech: Simultaneous Speech-to-speech Translation with Multi-task Learning, by Shaolei Zhang et al.
StreamSpeech: Simultaneous Speech-to-Speech Translation with Multi-task Learning
by Shaolei Zhang, Qingkai Fang, Shoutao Guo, Zhengrui Ma, Min Zhang, Yang Feng
First submitted to arxiv on: 5 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 paper proposes StreamSpeech, a unified framework for simultaneous speech-to-speech translation (Simul-S2ST) that learns both translation and policy simultaneously. This approach enables the model to perform offline and simultaneous speech recognition, speech translation, and speech synthesis seamlessly. The model achieves state-of-the-art performance in both offline S2ST and Simul-S2ST tasks on the CVSS benchmark. Additionally, StreamSpeech provides high-quality intermediate results during the simultaneous translation process, enhancing the overall real-time communication experience. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Simultaneous speech-to-speech translation is a critical technology for real-time communication. The proposed model, StreamSpeech, can translate between two languages while receiving speech inputs in real-time. This model learns to control the translation process and generate target speech at the right moment. It’s an “All-in-One” model that can also perform offline tasks like speech recognition, translation, and synthesis. The results show that StreamSpeech works well and is a significant step towards more effective real-time communication. |
Keywords
» Artificial intelligence » Translation
