@inproceedings{ortega-etal-2026-team,
title = "Team {QUESPA} System Submission for the {IWSLT} 2026 Dialectal and Low-resource Speech Translation Task",
author = "Ortega, John E. and
Zevallos, Rodolfo Joel and
Carraro, Fabr{\'i}cio and
S{\'a}nchez Bautista, Stephanny Gabriela and
Howe, Chad",
editor = "Salesky, Elizabeth and
Anastasopoulos, Antonios and
Negri, Matteo and
Federico, Marcello",
booktitle = "Proceedings of the 23rd International Conference on Spoken Language Translation ({IWSLT} 2026)",
month = jul,
year = "2026",
address = "San Diego, USA (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.iwslt-1.5/",
pages = "47--57",
ISBN = "979-8-89176-411-8",
abstract = "This paper describes the QUESPA team{'}s speech translation (ST) submissions for the Quechua to Spanish (QUE-SPA) track of the IWSLT 2026 Evaluation Campaign on dialectal and low-resource speech translation. The campaign supports a single submission category, namely unconstrained. This marks our fourth consecutive participation in the IWSLT shared task, building upon prior systems with substantial improvements. Our 2026 submission comprises three unconstrained-only systems. The best-performing system (contrastive{~}2) extends our strongest model from the previous year by leveraging a high-performing pre-trained language model (PLM) for end-to-end speech translation without cascading, augmented with additional Quechua-Collao text - now made available on the IWSLT GitHub. Fine-tuning Microsoft{'}s SpeechT5 model in an ST setting, combined with targeted data augmentation, results in a BLEU score of 27.2 on the official evaluation set. Additionally, we evaluate prompt-based machine translation using Gemini, DeepSeek, GPT-5, Claude, and Qwen for the first time. Aside from that, we introduce SIDON, an audio enhancement framework designed to improve audio quality. This paper provides a comparative analysis across our current and three previous IWSLT submissions, with a detailed examination of the impact of synthetic data, unconstrained external resources, and audio enhancement techniques on fine-tuning performance. Our results highlight the complementary role of PLM-based ST, LLM prompting, and ASR enhancement in advancing low-resource speech translation."
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<abstract>This paper describes the QUESPA team’s speech translation (ST) submissions for the Quechua to Spanish (QUE-SPA) track of the IWSLT 2026 Evaluation Campaign on dialectal and low-resource speech translation. The campaign supports a single submission category, namely unconstrained. This marks our fourth consecutive participation in the IWSLT shared task, building upon prior systems with substantial improvements. Our 2026 submission comprises three unconstrained-only systems. The best-performing system (contrastive 2) extends our strongest model from the previous year by leveraging a high-performing pre-trained language model (PLM) for end-to-end speech translation without cascading, augmented with additional Quechua-Collao text - now made available on the IWSLT GitHub. Fine-tuning Microsoft’s SpeechT5 model in an ST setting, combined with targeted data augmentation, results in a BLEU score of 27.2 on the official evaluation set. Additionally, we evaluate prompt-based machine translation using Gemini, DeepSeek, GPT-5, Claude, and Qwen for the first time. Aside from that, we introduce SIDON, an audio enhancement framework designed to improve audio quality. This paper provides a comparative analysis across our current and three previous IWSLT submissions, with a detailed examination of the impact of synthetic data, unconstrained external resources, and audio enhancement techniques on fine-tuning performance. Our results highlight the complementary role of PLM-based ST, LLM prompting, and ASR enhancement in advancing low-resource speech translation.</abstract>
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%0 Conference Proceedings
%T Team QUESPA System Submission for the IWSLT 2026 Dialectal and Low-resource Speech Translation Task
%A Ortega, John E.
%A Zevallos, Rodolfo Joel
%A Carraro, Fabrício
%A Sánchez Bautista, Stephanny Gabriela
%A Howe, Chad
%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 ortega-etal-2026-team
%X This paper describes the QUESPA team’s speech translation (ST) submissions for the Quechua to Spanish (QUE-SPA) track of the IWSLT 2026 Evaluation Campaign on dialectal and low-resource speech translation. The campaign supports a single submission category, namely unconstrained. This marks our fourth consecutive participation in the IWSLT shared task, building upon prior systems with substantial improvements. Our 2026 submission comprises three unconstrained-only systems. The best-performing system (contrastive 2) extends our strongest model from the previous year by leveraging a high-performing pre-trained language model (PLM) for end-to-end speech translation without cascading, augmented with additional Quechua-Collao text - now made available on the IWSLT GitHub. Fine-tuning Microsoft’s SpeechT5 model in an ST setting, combined with targeted data augmentation, results in a BLEU score of 27.2 on the official evaluation set. Additionally, we evaluate prompt-based machine translation using Gemini, DeepSeek, GPT-5, Claude, and Qwen for the first time. Aside from that, we introduce SIDON, an audio enhancement framework designed to improve audio quality. This paper provides a comparative analysis across our current and three previous IWSLT submissions, with a detailed examination of the impact of synthetic data, unconstrained external resources, and audio enhancement techniques on fine-tuning performance. Our results highlight the complementary role of PLM-based ST, LLM prompting, and ASR enhancement in advancing low-resource speech translation.
%U https://aclanthology.org/2026.iwslt-1.5/
%P 47-57
Markdown (Informal)
[Team QUESPA System Submission for the IWSLT 2026 Dialectal and Low-resource Speech Translation Task](https://aclanthology.org/2026.iwslt-1.5/) (Ortega et al., IWSLT 2026)
ACL