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The PACT framework advances speech-to-speech translation by introducing a pragmatic prompting mechanism that successfully transfers emotional tone, rhythm, and voice identity across languages while maintaining content fidelity. This approach addresses a critical gap in current models that often sacrifice expressive nuance for accuracy, offering a more robust solution for real-world conversational AI applications.
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PACT: A Pragmatic Prompting Framework for Expressive and Voice-Preserving Speech-to-Speech Translation

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arXiv:2610.18247v1 Announce Type: new Abstract: Speech-to-speech translation should preserve not only what is said, but also who says it and how it is said. Existing end-to-end S2ST, speech MLLM, and cascade systems can preserve content or timbre, but often struggle to realize emotion, tone, rhythm, pauses, and pragmatic attitude in the target language. We propose PACT, a target-space pragmatic prompting framework for expressive and voice-preserving S2ST. PACT extracts source pragmatic cues, projects them into the target-language expression space, calibrates the projected representation to a realizable target subspace, and converts it into continuous prompts for a frozen speech generator. On bidirectional Spanish-English TOPI evaluation, PACT achieves the best overall performance among evaluated systems across content fidelity, voice preservation, and expression preservation metrics.

Abstract:Speech-to-speech translation should preserve not only what is said, but also who says it and how it is said. Existing end-to-end S2ST, speech MLLM, and cascade systems can preserve content or timbre, but often struggle to realize emotion, tone, rhythm, pauses, and pragmatic attitude in the target language. We propose PACT, a target-space pragmatic prompting framework for expressive and voice-preserving S2ST. PACT extracts source pragmatic cues, projects them into the target-language expression space, calibrates the projected representation to a realizable target subspace, and converts it into continuous prompts for a frozen speech generator. On bidirectional Spanish-English TOPI evaluation, PACT achieves the best overall performance among evaluated systems across content fidelity, voice preservation, and expression preservation metrics.

Computation and Language (cs.CL)

arXiv:2610.18247 [cs.CL]

(or arXiv:2610.18247v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2610.18247 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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