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        <title>KIT’s Submission to Cross-Lingual Voice Cloning in IWSLT 2026</title>
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        <namePart type="given">Seymanur</namePart>
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            <title>Proceedings of the 23rd International Conference on Spoken Language Translation (IWSLT 2026)</title>
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        <name type="personal">
            <namePart type="given">Elizabeth</namePart>
            <namePart type="family">Salesky</namePart>
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            <namePart type="given">Antonios</namePart>
            <namePart type="family">Anastasopoulos</namePart>
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        <name type="personal">
            <namePart type="given">Matteo</namePart>
            <namePart type="family">Negri</namePart>
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        <name type="personal">
            <namePart type="given">Marcello</namePart>
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    <abstract>Cross-lingual voice cloning aims to generate speech in a target language while preserving speaker identity from a source-language reference. This task is central to speech translation and is the focus of the IWSLT 2026 Cross-Lingual Voice Cloning track. A key challenge is maintaining intelligibility and naturalness in the presence of accent variation and domain-specific vocabulary. We build on a multilingual text-to-speech model, FishAudio-S2-Pro, and introduce language tag prompting to improve language control and reduce accent leakage. We further apply reinforcement learning (RL) fine-tuning for task adaptation and observe improvements in intelligibility. Finally, we propose a reference-conditioned lexical matching method that improves pronunciation of domain-specific terms when lexical overlap is present. Results show that language prompting provides the largest gains, while lexical matching yields consistent improvements on matched subsets.</abstract>
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%0 Conference Proceedings
%T KIT’s Submission to Cross-Lingual Voice Cloning in IWSLT 2026
%A Akti, Seymanur
%A Waibel, Alexander
%Y Salesky, Elizabeth
%Y Anastasopoulos, Antonios
%Y Negri, Matteo
%Y Federico, Marcello
%S Proceedings of the 23rd International Conference on Spoken Language Translation (IWSLT 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, USA (in-person and online)
%@ 979-8-89176-411-8
%F akti-waibel-2026-kits
%X Cross-lingual voice cloning aims to generate speech in a target language while preserving speaker identity from a source-language reference. This task is central to speech translation and is the focus of the IWSLT 2026 Cross-Lingual Voice Cloning track. A key challenge is maintaining intelligibility and naturalness in the presence of accent variation and domain-specific vocabulary. We build on a multilingual text-to-speech model, FishAudio-S2-Pro, and introduce language tag prompting to improve language control and reduce accent leakage. We further apply reinforcement learning (RL) fine-tuning for task adaptation and observe improvements in intelligibility. Finally, we propose a reference-conditioned lexical matching method that improves pronunciation of domain-specific terms when lexical overlap is present. Results show that language prompting provides the largest gains, while lexical matching yields consistent improvements on matched subsets.
%U https://aclanthology.org/2026.iwslt-1.8/
%P 78-83

Markdown (Informal)

[KIT’s Submission to Cross-Lingual Voice Cloning in IWSLT 2026](https://aclanthology.org/2026.iwslt-1.8/) (Akti & Waibel, IWSLT 2026)

• KIT’s Submission to Cross-Lingual Voice Cloning in IWSLT 2026 (Akti & Waibel, IWSLT 2026)

ACL

• Seymanur Akti and Alexander Waibel. 2026. KIT’s Submission to Cross-Lingual Voice Cloning in IWSLT 2026. In Proceedings of the 23rd International Conference on Spoken Language Translation (IWSLT 2026), pages 78–83, San Diego, USA (in-person and online). Association for Computational Linguistics.