The article presents SEAMLESSEXPRESSIVELM, an innovative language model designed for expressive speech-to-speech translation (S2ST), which optimally preserves semantics and vocal styles. Unlike prior methods relying on aligned data, this model leverages succinct language modeling and breaks down translation processes into sequential steps, starting with semantic content followed by style transfer. Evaluated on Spanish-to-English and Hungarian-to-English translations, SEAMLESSEXPRESSIVELM showcases notable advancements in both semantic fidelity and acoustic styling, outperforming previous cascaded models in efficiency and quality.
Recent studies have begun to leverage the advancements in language modeling to create cascaded LMs that effectively integrate semantic and acoustic information, yielding significant improvements in translation outcomes.
SEAMLESSEXPRESSIVELM breaks down the complex S2ST mapping into intermediate steps using chain-of-thought prompting, improving both semantic quality and speaker style transfer.
#speech-translation #language-modeling #expressive-communication #semantic-preservation #acoustic-information
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