@inproceedings{zhang-etal-2025-leveraging-unit,
title = "Leveraging Unit Language Guidance to Advance Speech Modeling in Textless Speech-to-Speech Translation",
author = "Zhang, Yuhao and
Ma, Xiangnan and
Kou, Kaiqi and
Liu, Peizhuo and
Shan, Weiqiao and
Wang, Benyou and
Xiao, Tong and
Huang, Yuxin and
Yu, Zhengtao and
Zhu, JingBo",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.75/",
doi = "10.18653/v1/2025.findings-acl.75",
pages = "1448--1460",
ISBN = "979-8-89176-256-5",
abstract = "The success of building textless speech-to-speech translation (S2ST) models has attracted much attention. However, S2ST still faces two main challenges: 1) extracting linguistic features for various speech signals, called cross-modal (CM), and 2) learning alignment of difference languages in long sequences, called cross-lingual (CL). We propose the unit language to overcome the two modeling challenges. The unit language can be considered a text-like representation format, constructed using $n$-gram language modeling. We implement multi-task learning to utilize the unit language in guiding the speech modeling process. Our initial results reveal a conflict when applying source and target unit languages simultaneously. We propose task prompt modeling to mitigate this conflict. We conduct experiments on four languages of the Voxpupil dataset. Our method demonstrates significant improvements over a strong baseline and achieves performance comparable to models trained with text."
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<abstract>The success of building textless speech-to-speech translation (S2ST) models has attracted much attention. However, S2ST still faces two main challenges: 1) extracting linguistic features for various speech signals, called cross-modal (CM), and 2) learning alignment of difference languages in long sequences, called cross-lingual (CL). We propose the unit language to overcome the two modeling challenges. The unit language can be considered a text-like representation format, constructed using n-gram language modeling. We implement multi-task learning to utilize the unit language in guiding the speech modeling process. Our initial results reveal a conflict when applying source and target unit languages simultaneously. We propose task prompt modeling to mitigate this conflict. We conduct experiments on four languages of the Voxpupil dataset. Our method demonstrates significant improvements over a strong baseline and achieves performance comparable to models trained with text.</abstract>
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%0 Conference Proceedings
%T Leveraging Unit Language Guidance to Advance Speech Modeling in Textless Speech-to-Speech Translation
%A Zhang, Yuhao
%A Ma, Xiangnan
%A Kou, Kaiqi
%A Liu, Peizhuo
%A Shan, Weiqiao
%A Wang, Benyou
%A Xiao, Tong
%A Huang, Yuxin
%A Yu, Zhengtao
%A Zhu, JingBo
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F zhang-etal-2025-leveraging-unit
%X The success of building textless speech-to-speech translation (S2ST) models has attracted much attention. However, S2ST still faces two main challenges: 1) extracting linguistic features for various speech signals, called cross-modal (CM), and 2) learning alignment of difference languages in long sequences, called cross-lingual (CL). We propose the unit language to overcome the two modeling challenges. The unit language can be considered a text-like representation format, constructed using n-gram language modeling. We implement multi-task learning to utilize the unit language in guiding the speech modeling process. Our initial results reveal a conflict when applying source and target unit languages simultaneously. We propose task prompt modeling to mitigate this conflict. We conduct experiments on four languages of the Voxpupil dataset. Our method demonstrates significant improvements over a strong baseline and achieves performance comparable to models trained with text.
%R 10.18653/v1/2025.findings-acl.75
%U https://aclanthology.org/2025.findings-acl.75/
%U https://doi.org/10.18653/v1/2025.findings-acl.75
%P 1448-1460
Markdown (Informal)
[Leveraging Unit Language Guidance to Advance Speech Modeling in Textless Speech-to-Speech Translation](https://aclanthology.org/2025.findings-acl.75/) (Zhang et al., Findings 2025)
ACL
- Yuhao Zhang, Xiangnan Ma, Kaiqi Kou, Peizhuo Liu, Weiqiao Shan, Benyou Wang, Tong Xiao, Yuxin Huang, Zhengtao Yu, and JingBo Zhu. 2025. Leveraging Unit Language Guidance to Advance Speech Modeling in Textless Speech-to-Speech Translation. In Findings of the Association for Computational Linguistics: ACL 2025, pages 1448–1460, Vienna, Austria. Association for Computational Linguistics.