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        <title>One Voice, Many Tongues: Cross-Lingual Voice Cloning for Scientific Speech</title>
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    <abstract>Preserving a speaker’s voice identity while generating speech in a different language remains a fundamental challenge in spoken language technology, particularly in specialized domains such as scientific communication. In this paper, we address this challenge through our system submission to the International Conference on Spoken Language Translation (IWSLT 2026), the Cross-Lingual Voice Cloning shared task. First, we evaluate several state-of-the-art voice cloning models for cross-lingual speech generation of scientific texts in Arabic, Chinese, and French. Then, we build voice cloning systems based on the OmniVoice foundation model. We employ data augmentation via multi-model ensemble distillation from the ACL 60/60 corpus. We investigate the effect of using this synthetic data for fine-tuning, demonstrating improvements in intelligibility (WER and CER) and speaker similarity (SIM), with gains varying across languages.</abstract>
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%0 Conference Proceedings
%T One Voice, Many Tongues: Cross-Lingual Voice Cloning for Scientific Speech
%A Abebe, Amanuel Gizachew
%A Moslem, Yasmin
%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 abebe-moslem-2026-one
%X Preserving a speaker’s voice identity while generating speech in a different language remains a fundamental challenge in spoken language technology, particularly in specialized domains such as scientific communication. In this paper, we address this challenge through our system submission to the International Conference on Spoken Language Translation (IWSLT 2026), the Cross-Lingual Voice Cloning shared task. First, we evaluate several state-of-the-art voice cloning models for cross-lingual speech generation of scientific texts in Arabic, Chinese, and French. Then, we build voice cloning systems based on the OmniVoice foundation model. We employ data augmentation via multi-model ensemble distillation from the ACL 60/60 corpus. We investigate the effect of using this synthetic data for fine-tuning, demonstrating improvements in intelligibility (WER and CER) and speaker similarity (SIM), with gains varying across languages.
%U https://aclanthology.org/2026.iwslt-1.25/
%P 227-231

Markdown (Informal)

[One Voice, Many Tongues: Cross-Lingual Voice Cloning for Scientific Speech](https://aclanthology.org/2026.iwslt-1.25/) (Abebe & Moslem, IWSLT 2026)

• One Voice, Many Tongues: Cross-Lingual Voice Cloning for Scientific Speech (Abebe & Moslem, IWSLT 2026)

ACL

• Amanuel Gizachew Abebe and Yasmin Moslem. 2026. One Voice, Many Tongues: Cross-Lingual Voice Cloning for Scientific Speech. In Proceedings of the 23rd International Conference on Spoken Language Translation (IWSLT 2026), pages 227–231, San Diego, USA (in-person and online). Association for Computational Linguistics.