StreamSpeech: Simultaneous Speech-to-Speech Translation with Multi-task Learning
<|reference_start|>StreamSpeech: Simultaneous Speech-to-Speech Translation with Multi-task Learning: Simultaneous speech-to-speech translation (Simul-S2ST, a.k.a streaming speech translation) outputs target speech while receiving streaming speech inputs, which is critical for real-time communication. Beyond accomplishing translation between speech, Simul-S2ST requires a policy to control the model to generate corresponding target speech at the opportune moment within speech inputs, thereby posing a double challenge of translation and policy. In this paper, we propose StreamSpeech, a direct Simul-S2ST model that jointly learns translation and simultaneous policy in a unified framework of multi-task learning. Adhering to a multi-task learning approach, StreamSpeech can perform offline and simultaneous speech recognition, speech translation and speech synthesis via an "All-in-One" seamless model. Experiments on CVSS benchmark demonstrate that StreamSpeech achieves state-of-the-art performance in both offline S2ST and Simul-S2ST tasks. Besides, StreamSpeech is able to present high-quality intermediate results (i.e., ASR or translation results) during simultaneous translation process, offering a more comprehensive real-time communication experience.<|reference_end|>
@article{zhang2024streamspeech:,
    title={StreamSpeech: Simultaneous Speech-to-Speech Translation with Multi-task
  Learning},
    author={Shaolei Zhang, Qingkai Fang, Shoutao Guo, Zhengrui Ma, Min Zhang, Yang
  Feng},
    journal={arXiv preprint arXiv:2406.03049},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.03049},
    primaryClass={cs.CL cs.AI cs.SD eess.AS}
}