MSLM-S2ST: A Multitask Speech Language Model for Textless Speech-to-Speech Translation with Speaker Style Preservation
<|reference_start|>MSLM-S2ST: A Multitask Speech Language Model for Textless Speech-to-Speech Translation with Speaker Style Preservation: There have been emerging research interest and advances in speech-to-speech translation (S2ST), translating utterances from one language to another. This work proposes Multitask Speech Language Model (MSLM), which is a decoder-only speech language model trained in a multitask setting. Without reliance on text training data, our model is able to support multilingual S2ST with speaker style preserved.<|reference_end|>
@article{peng2024mslm-s2st:,
    title={MSLM-S2ST: A Multitask Speech Language Model for Textless
  Speech-to-Speech Translation with Speaker Style Preservation},
    author={Yifan Peng, Ilia Kulikov, Yilin Yang, Sravya Popuri, Hui Lu, Changhan
  Wang, Hongyu Gong},
    journal={arXiv preprint arXiv:2403.12408},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.12408},
    primaryClass={cs.CL cs.SD eess.AS}
}