CTC Regularization for Low-Resource Speech-to-Text Translation

Zachary William Hopton; Rico Sennrich

CTC Regularization for Low-Resource Speech-to-Text Translation

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Abstract

The challenges of building speech-to-text translation (ST) systems (e.g., a relative lack of parallel speech–text data and robustness to noise in audio) are exacerbated for low-resource language pairs. In this work, we seek to improve low-resource ST by building on previous studies that regularize ST training with the connectionist temporal classification (CTC) loss. By systematically evaluating a diverse range of linguistic annotations as CTC labels across multiple auxiliary loss configurations, we improve speech translation systems for both low- and high-resource settings. These improvements over both a standard end-to-end ST system and a speech LLM indicate a need for continued research on regularizing speech representations in ST.

Anthology ID:: 2026.loresmt-1.15
Volume:: Proceedings for the Ninth Workshop on Technologies for Machine Translation of Low Resource Languages (LoResMT 2026)
Month:: March
Year:: 2026
Address:: Rabat, Morocco
Editors:: Atul Kr. Ojha, Chao-hong Liu, Ekaterina Vylomova, Flammie Pirinen, Jonathan Washington, Nathaniel Oco, Xiaobing Zhao
Venues:: LoResMT | WS
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 186–197
Language:
URL:: https://aclanthology.org/2026.loresmt-1.15/
DOI:
Bibkey:
Cite (ACL):: Zachary William Hopton and Rico Sennrich. 2026. CTC Regularization for Low-Resource Speech-to-Text Translation. In Proceedings for the Ninth Workshop on Technologies for Machine Translation of Low Resource Languages (LoResMT 2026), pages 186–197, Rabat, Morocco. Association for Computational Linguistics.
Cite (Informal):: CTC Regularization for Low-Resource Speech-to-Text Translation (Hopton & Sennrich, LoResMT 2026)
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PDF:: https://aclanthology.org/2026.loresmt-1.15.pdf

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@inproceedings{hopton-sennrich-2026-ctc,
    title = "{CTC} Regularization for Low-Resource Speech-to-Text Translation",
    author = "Hopton, Zachary William  and
      Sennrich, Rico",
    editor = "Ojha, Atul Kr.  and
      Liu, Chao-hong  and
      Vylomova, Ekaterina  and
      Pirinen, Flammie  and
      Washington, Jonathan  and
      Oco, Nathaniel  and
      Zhao, Xiaobing",
    booktitle = "Proceedings for the Ninth Workshop on Technologies for Machine Translation of Low Resource Languages ({L}o{R}es{MT} 2026)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.loresmt-1.15/",
    pages = "186--197",
    ISBN = "979-8-89176-366-1",
    abstract = "The challenges of building speech-to-text translation (ST) systems (e.g., a relative lack of parallel speech{--}text data and robustness to noise in audio) are exacerbated for low-resource language pairs. In this work, we seek to improve low-resource ST by building on previous studies that regularize ST training with the connectionist temporal classification (CTC) loss. By systematically evaluating a diverse range of linguistic annotations as CTC labels across multiple auxiliary loss configurations, we improve speech translation systems for both low- and high-resource settings. These improvements over both a standard end-to-end ST system and a speech LLM indicate a need for continued research on regularizing speech representations in ST."
}

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%0 Conference Proceedings
%T CTC Regularization for Low-Resource Speech-to-Text Translation
%A Hopton, Zachary William
%A Sennrich, Rico
%Y Ojha, Atul Kr.
%Y Liu, Chao-hong
%Y Vylomova, Ekaterina
%Y Pirinen, Flammie
%Y Washington, Jonathan
%Y Oco, Nathaniel
%Y Zhao, Xiaobing
%S Proceedings for the Ninth Workshop on Technologies for Machine Translation of Low Resource Languages (LoResMT 2026)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-366-1
%F hopton-sennrich-2026-ctc
%X The challenges of building speech-to-text translation (ST) systems (e.g., a relative lack of parallel speech–text data and robustness to noise in audio) are exacerbated for low-resource language pairs. In this work, we seek to improve low-resource ST by building on previous studies that regularize ST training with the connectionist temporal classification (CTC) loss. By systematically evaluating a diverse range of linguistic annotations as CTC labels across multiple auxiliary loss configurations, we improve speech translation systems for both low- and high-resource settings. These improvements over both a standard end-to-end ST system and a speech LLM indicate a need for continued research on regularizing speech representations in ST.
%U https://aclanthology.org/2026.loresmt-1.15/
%P 186-197

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Markdown (Informal)

[CTC Regularization for Low-Resource Speech-to-Text Translation](https://aclanthology.org/2026.loresmt-1.15/) (Hopton & Sennrich, LoResMT 2026)

CTC Regularization for Low-Resource Speech-to-Text Translation (Hopton & Sennrich, LoResMT 2026)

ACL

Zachary William Hopton and Rico Sennrich. 2026. CTC Regularization for Low-Resource Speech-to-Text Translation. In Proceedings for the Ninth Workshop on Technologies for Machine Translation of Low Resource Languages (LoResMT 2026), pages 186–197, Rabat, Morocco. Association for Computational Linguistics.