Expressive speech-to-speech translation (S2ST) is a key research topic in seamless communication, which focuses on the preservation of semantics and speaker vocal style in translated speech. Early works synthesized speaker style aligned speech in order to directly learn the mapping from speech to target speech spectrogram. Without reliance on style aligned data, recent studies leverage the advances of language modeling (LM) and build cascaded LMs on semantic and acoustic tokens. This work proposes SeamlessExpressiveLM, a single speech language model for expressive S2ST. We decompose the complex source-to-target speech mapping into intermediate generation steps with chain-of-thought prompting. The model is first guided to translate target semantic content and then transfer the speaker style to multi-stream acoustic units. Evaluated on Spanish-to-English and Hungarian-to-English translations, SeamlessExpressiveLM outperforms cascaded LMs in both semantic quality and style transfer, meanwhile achieving better parameter efficiency.

提出了SeamlessExpressiveLM，一个用于表情化语音到语音翻译的单一语言模型，通过分解复杂的源到目标语音映射为中间生成步骤，并结合思维链引导模型，实现了在西班牙语到英语和匈牙利语到英语翻译中优于级联语言模型的语义质量和风格转移效果，同时实现了更好的参数效率。

SeamlessExpressiveLM: 链条思维情感语言模型用于表达性语音到语音翻译