MSLM-S2ST: A Multitask Speech Language Model for Textless
  Speech-to-Speech Translation with Speaker Style Preservation
Yifan Peng Ilia Kulikov Yilin Yang Sravya Popuri Hui Lu Changhan Wang Hongyu Gong
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.