GenAI4ELab/papercli-papers huggingface dataset:
Kuvost: A Large-Scale Human-Annotated English to Central Kurdish Speech Translation Dataset Driven from English Common Voice
Authors: Mohammad Mohammadamini; Daban Jaff; Sara Jamal; Ibrahim Ahmed; Hawkar Omar; Darya Sabr; Marie Tahon; Antoine Laurent
Venue: ACL 2025 (Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025))

Abstract: In this paper, we introduce the Kuvost, a large-scale English to Central Kurdish speech-to-text-translation (S2TT) dataset. This dataset includes 786k utterances derived from Common Voice 18, translated and revised by 230 volunteers into Central Kurdish. Encompassing 1,003 hours of translated speech, this dataset can play a groundbreaking role for Central Kurdish, which severely lacks public-domain resources for speech translation. Following the dataset division in Common Voice, there are 298k, 6,226, and 7,253 samples in the train, development, and test sets, respectively. The dataset is evaluated on end-to-end English-to-Kurdish S2TT using Whisper V3 Large and SeamlessM4T V2 Large models. The dataset is available under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License https://huggingface.co/datasets/aranemini/kuvost.
Literary Translations and Synthetic Data for Machine Translation of Low-resourced Middle Eastern Languages
Authors: Sina Ahmadi; Razhan Hameed; Rico Sennrich
Venue: ACL 2025 (Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025))

Abstract: Middle Eastern languages represent a linguistically diverse landscape, yet few have received substantial attention in language and speech technology outside those with official status. Machine translation, a cornerstone application in computational linguistics, remains particularly underexplored for these predominantly non-standardized, spoken varieties. This paper proposes data alignment and augmentation techniques that leverage monolingual corpora and large language models to create high-quality parallel corpora for low-resource Middle Eastern languages. Through systematic fine-tuning of a pretrained machine translation model in a multilingual framework, our results demonstrate that corpus quality consistently outperforms quantity as a determinant of translation accuracy. Furthermore, we provide empirical evidence that strategic data selection significantly enhances cross-lingual transfer in multilingual translation systems. These findings offer valuable insights for developing machine translation solutions in linguistically diverse, resource-constrained environments.
Prompting LLMs: Length Control for Isometric Machine Translation
Authors: Dávid Javorský; Ondřej Bojar; François Yvon
Venue: ACL 2025 (Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025))

Abstract: In this study, we explore the effectiveness of isometric machine translation across multiple language pairs (EnoDe, EnoFr, and EnoEs) under the conditions of the IWSLT Isometric Shared Task 2022. Using eight open-source large language models (LLMs) of varying sizes, we investigate how different prompting strategies, varying numbers of few-shot examples, and demonstration selection influence translation quality and length control. We discover that the phrasing of instructions, when aligned with the properties of the provided demonstrations, plays a crucial role in controlling the output length. Our experiments show that LLMs tend to produce shorter translations only when presented with extreme examples, while isometric demonstrations often lead to the models disregarding length constraints. While few-shot prompting generally enhances translation quality, further improvements are marginal across 5, 10, and 20-shot settings. Finally, considering multiple outputs allows to notably improve overall tradeoff between the length and quality, yielding state-of-the-art performance for some language pairs.
Human-Evaluated Urdu-English Speech Corpus: Advancing Speech-to-Text for Low-Resource Languages
Authors: Humaira Mehmood; Sadaf Abdul Rauf
Venue: ACL 2025 (Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025))

Abstract: This paper presents our contribution to the IWSLT Low Resource Track 2: ‘Training and Evaluation Data Track’. We share a human-evaluated Urdu-English speech-to-text corpus based on Common Voice 13.0 Urdu speech corpus. We followed a three-tier validation scheme which involves an initial automatic translation with corrections from native reviewers, full review by evaluators followed by final validation from a bilingual expert ensuring reliable corpus for subsequent NLP tasks. Our contribution, CV-UrEnST corpus, enriches Urdu speech resources by contributing the first Urdu-English speech-to-text corpus. When evaluated with Whisper-medium, the corpus yielded a significant improvement to the vanilla model in terms of BLEU, chrF++, and COMET scores, demonstrating its effectiveness for speech translation tasks.
FFSTC 2: Extending the Fongbe to French Speech Translation Corpus
Authors: D. Fortuné KPONOU; Salima Mdhaffar; Fréjus A. A. Laleye; Eugène Cokou Ezin; Yannick Estève
Venue: ACL 2025 (Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025))

Abstract: This paper introduced FFSTC 2, an expanded version of the existing Fongbe-to-French speech translation corpus, addressing the critical need for resources in African dialects for speech recognition and translation tasks. We extended the dataset by adding 36 hours of transcribed audio, bringing the total to 61 hours, thereby enhancing its utility for both automatic speech recognition (ASR) and speech translation (ST) in Fongbe, a low-resource language. Using this enriched corpus, we developed both cascade and end-to-end speech translation systems. Our models employ AfriHuBERT and HuBERT147, two speech encoders specialized to African languages, and the NLLB and mBART models as decoders. We also investigate the use of the SAMU-XLSR approach to inject sentence-level semantic information to the XSLR-128 model used as an alternative speech encoder. We also introduced a novel diacritic-substitution technique for ASR, which, when combined with NLLB, enables a cascade model to achieve a BLEU score of 37.23 ompared to 39.60 obtained by the best system using original diacritics. Among the end-to-end architectures evaluated, the architectures with data augmentation and NLLB as decoder achieved the highest score respectively, SAMU-NLLB scored the BLEU score of 28.43.
HENT-SRT: Hierarchical Efficient Neural Transducer with Self-Distillation for Joint Speech Recognition and Translation
Authors: Amir Hussein; Cihan Xiao; Matthew Wiesner; Dan Povey; Leibny Paola Garcia; Sanjeev Khudanpur
Venue: ACL 2025 (Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025))

Abstract: Neural transducers (NT) provide an effective framework for speech streaming, demonstrating strong performance in automatic speech recognition (ASR). However, the application of NT to speech translation (ST) remains challenging, as existing approaches struggle with word reordering and performance degradation when jointly modeling ASR and ST, resulting in a gap with attention-based encoder-decoder (AED) models. Existing NT-based ST approaches also suffer from high computational training costs. To address these issues, we propose HENT-SRT (Hierarchical Efficient Neural Transducer for Speech Recognition and Translation), a novel framework that factorizes ASR and translation tasks to better handle reordering. To ensure robust ST while preserving ASR performance, we use self-distillation with CTC consistency regularization. Moreover, we improve computational efficiency by incorporating best practices from ASR transducers, including a down-sampled hierarchical encoder, a stateless predictor, and a pruned transducer loss to reduce training complexity. Finally, we introduce a blank penalty during decoding, reducing deletions and improving translation quality. Our approach is evaluated on three conversational datasets Arabic, Spanish, and Mandarin achieving new state-of-the-art performance among NT models and substantially narrowing the gap with AED-based systems.
Swiss German Speech Translation and the Curse of Multidialectality
Authors: Martin Bär; Andrea DeMarco; Gorka Labaka
Venue: ACL 2025 (Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025))

Abstract: In many languages, non-standardized varieties make the development of NLP models challenging. This paper explores various fine-tuning techniques and data setups for training Swiss German to Standard German speech-to-text translation models. While fine-tuning on all available Swiss German data yields the best results, ASR pre-training lowers performance by 1.48 BLEU points, and jointly training on Swiss and Standard German data reduces it by 2.29 BLEU. Our dialect transfer experiments suggest that an equivalent of the Curse of Multilinguality (Conneau et al., 2020) exists in dialectal speech processing, as training on multiple dialects jointly tends to decrease single-dialect performance. However, introducing small amounts of dialectal variability can improve the performance for low-resource dialects.
CDAC-SVNIT submission for IWSLT 2025 Indic track shared task
Authors: Mukund K. Roy; Karunesh Arora; Praveen Kumar Chandaliya; Rohit Kumar; Pruthwik Mishra
Venue: ACL 2025 (Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025))

Abstract: In this paper, we designed a Speech-to-Text Translation (ST) system to translate English into Hindi, Bengali, and Tamil, and vice versa. We explored both cascaded and End-to-End (E2E) approaches as part of the IWSLT 2025 Indic shared task.
NAVER LABS Europe Submission to the Instruction-following Track
Authors: Beomseok Lee; Marcely Zanon Boito; Laurent Besacier; Ioan Calapodescu
Venue: ACL 2025 (Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025))

Abstract: In this paper we describe NAVER LABS Europe submission to the instruction-following speech processing short track at IWSLT 2025. We participate in the constrained settings, developing systems that can simultaneously perform ASR, ST, and SQA tasks from English speech input into the following target languages: Chinese, Italian, and German. Our solution leverages two pretrained modules: (1) a speech-to-LLM embedding projector trained using representations from the SeamlessM4T-v2-large speech encoder; and (2) LoRA adapters trained on text data on top of Llama-3.1-8B-Instruct. These modules are jointly loaded and further instruction-tuned for 1K steps on multilingual and multimodal data to form our final system submitted for evaluation.
JU-CSE-NLP’s Cascaded Speech to Text Translation Systems for IWSLT 2025 in Indic Track
Authors: Debjit Dhar; Soham Lahiri; Tapabrata Mondal; Sivaji Bandyopadhyay
Venue: ACL 2025 (Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025))

Abstract: This paper presents the submission of the Jadavpur University Computer Science and Engineering Natural Language Processing (JU-CSENLP) Laboratory to the International Conference on Spoken Language Translation (IWSLT) 2025 Indic track, addressing the speech-to-text translation task in both English-to-Indic (Bengali, Hindi, Tamil) and Indic-to-English directions. To tackle the challenges posed by low resource Indian languages, we adopt a cascaded approach leveraging state-of-the-art pre-trained models. For English-to-Indic translation, we utilize OpenAI’s Whisper model for Automatic Speech Recognition (ASR), followed by the Meta’s No Language Left Behind (NLLB)-200-distilled-600M model finetuned for Machine Translation (MT). For the reverse direction, we employ the AI4Bharat’s IndicConformer model for ASR and IndicTrans2 finetuned for MT. Our models are fine-tuned on the provided benchmark dataset to better handle the linguistic diversity and domain-specific variations inherent in the data. Evaluation results demonstrate that our cascaded systems achieve competitive performance, with notable BLEU and chrF++ scores across all language pairs. Our findings highlight the effectiveness of combining robust ASR and MT components in a cascaded pipeline, particularly for low-resource and morphologically rich Indian languages.
NYA’s Offline Speech Translation System for IWSLT 2025
Authors: Wenxuan Wang; Yingxin Zhang; Yifan Jin; Binbin Du; Yuke Li
Venue: ACL 2025 (Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025))

Abstract: This paper reports NYA’s submissions to the IWSLT 2025 Offline Speech Translation (ST) task. The task includes three translation directions: English to Chinese, German, and Arabic. In detail, we adopt a cascaded speech translation architecture comprising automatic speech recognition (ASR) and machine translation (MT) components to participate in the unconstrained training track. For the ASR model, we use the Whisper medium model. For the neural machine translation (NMT) model, the wider and deeper Transformer is adopted as the backbone model. Building upon last year’s work, we implement multiple techniques and strategies such as data augmentation, domain adaptation, and model ensemble to improve the translation quality of the NMT model. In addition, we adopt X-ALMA as the foundational LLM-based MT model, with domain-specific supervised fine-tuning applied to train and optimize our LLM-based MT model. Finally, by employing COMET-based Minimum Bayes Risk decoding to integrate and select translation candidates from both NMT and LLM-based MT systems, the translation quality of our ST system is significantly improved, and competitive results are obtained on the evaluation set.
KIT’s Low-resource Speech Translation Systems for IWSLT2025: System Enhancement with Synthetic Data and Model Regularization
Authors: Zhaolin Li; Yining Liu; Danni Liu; Tuan Nam Nguyen; Enes Yavuz Ugan; Tu Anh Dinh; Carlos Mullov; Alexander Waibel; Jan Niehues
Venue: ACL 2025 (Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025))

Abstract: This paper presents KIT’s submissions to the IWSLT 2025 low-resource track. We develop both cascaded systems, consisting of Automatic Speech Recognition (ASR) and Machine Translation (MT) models, and end-to-end (E2E) Speech Translation (ST) systems for three language pairs: Bemba, North Levantine Arabic, and Tunisian Arabic into English. Building upon pre-trained models, we fine-tune our systems with different strategies to utilize resources efficiently. This study further explores system enhancement with synthetic data and model regularization. Specifically, we investigate MT-augmented ST by generating translations from ASR data using MT models. For North Levantine, which lacks parallel ST training data, a system trained solely on synthetic data slightly surpasses the cascaded system trained on real data. We also explore augmentation using text-to-speech models by generating synthetic speech from MT data, demonstrating the benefits of synthetic data in improving both ASR and ST performance for Bemba. Additionally, we apply intra-distillation to enhance model performance. Our experiments show that this approach consistently improves results across ASR, MT, and ST tasks, as well as across different pre-trained models. Finally, we apply Minimum Bayes Risk decoding to combine the cascaded and end-to-end systems, achieving an improvement of approximately 1.5 BLEU points.
AppTek’s Automatic Speech Translation: Generating Accurate and Well-Readable Subtitles
Authors: Frithjof Petrick; Patrick Wilken; Evgeny Matusov; Nahuel Unai Roselló Beneitez; Sarah Beranek
Venue: ACL 2025 (Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025))

Abstract: We describe AppTek’s submission to the subtitling track of the IWSLT 2025 evaluation. We enhance our cascaded speech translation approach by adapting the ASR and the MT models on in-domain data. All components, including intermediate steps such as subtitle source language template creation and line segmentation, are optimized to ensure that the resulting target language subtitles respect the subtitling constraints not only on the number of characters per line and the number of lines in each subtitle block, but also with respect to the desired reading speed. AppTek’s machine translation with length control plays the key role in this process, effectively condensing subtitles to these constraints. Our experiments show that this condensation results in high-quality translations that convey the most important information, as measured by metrics such as BLEU or BLEURT, as well as the primary metric subtitle edit rate (SubER).
KIT’s Offline Speech Translation and Instruction Following Submission for IWSLT 2025
Authors: Sai Koneru; Maike Züfle; Thai Binh Nguyen; Seymanur Akti; Jan Niehues; Alexander Waibel
Venue: ACL 2025 (Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025))

Abstract: In this paper, we present the submissions for the Offline ST and Instruction Following (IF) tracks, where we leverage LLMs to enhance performance across all tasks. For the Offline ST track, we propose a pipeline that employs multiple automatic speech recognition systems, whose outputs are fused using an LLM with document-level context. This is followed by a two-step translation process, incorporating additional contextual refinement step to improve translation quality. For the IF track, we develop an end-to-end model that integrates a speech encoder with an LLM to perform a wide range of instruction-following tasks. We complement it with a final document-level refinement stage to further enhance output quality by using contextual information.
IWSLT 2025 Indic Track System Description Paper: Speech-to-Text Translation from Low-Resource Indian Languages (Bengali and Tamil) to English
Authors: Sayan Das; Soham Chaudhuri; Dipanjan Saha; Dipankar Das; Sivaji Bandyopadhyay
Venue: ACL 2025 (Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025))

Abstract: Multi-language Speech-to-Text Translation (ST) plays a crucial role in breaking linguistic barriers, particularly in multilingual regions like India. This paper focuses on building a robust ST system for low resource Indian languages, with a special emphasis on Bengali and Tamil. These languages represent the Indo-Aryan and Dravidian families, respectively. The dataset used in this work comprises spoken content from TED Talks and conferences, paired with transcriptions in English and their translations in Bengali and Tamil. Our work specifically addresses the translation of Bengali and Tamil speech to English text, a critical area given the scarcity of annotated speech data. To enhance translation quality and model robustness, we leverage cross-lingual resources and word level translation strategies. The ultimate goal is to develop an end-to-end ST model capable of real-world deployment for under represented languages.
ALADAN at IWSLT25 Low-resource Arabic Dialectal Speech Translation Task
Authors: Josef Jon; Waad Ben Kheder; André Beyer; Claude Barras; Jean-Luc Gauvain
Venue: ACL 2025 (Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025))

Abstract: We present our IWSLT 2025 submission for the low-resource track on North Levantine Arabic to English speech translation, building on our IWSLT 2024 efforts. We retain last year’s cascade ASR architecture that combines a TDNN-F model and a Zipformer for the ASR step. We upgrade the Zipformer to the Zipformer-Large variant (253 M parameters vs. 66 M) to capture richer acoustic representations. For the MT part, to further alleviate data sparsity, we created a crowd-sourced parallel corpus covering five major Arabic dialects (Tunisian, Levantine, Moroccan, Algerian, Egyptian) curated via rigorous qualification and filtering. We show that using crowd-sourced data is feasible in low-resource scenarios as we observe improved automatic evaluation metrics across all dialects. We also experimented with the dataset under a high-resource scenario, where we had access to a large, high-quality Levantine Arabic corpus from LDC. In this setting, adding the crowd-sourced data does not improve the scores on the official validation set anymore. Our final submission scores 20.0 BLEU on the official test set.
QUESPA Submission for the IWSLT 2025 Dialectal and Low-resource Speech Translation Task
Authors: John E. Ortega; Rodolfo Joel Zevallos; William Chen; Idris Abdulmumin
Venue: ACL 2025 (Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025))

Abstract: This article describes the QUESPA team speech translation (ST) submissions for the Quechua to Spanish (QUE-SPA) track featured in the Evaluation Campaign of IWSLT 2025: dialectal and low-resource speech translation. This year, there is one main submission type supported in the campaign: unconstrained. This is our third year submitting our ST systems to the IWSLT shared task and we feel that we have achieved novel performance, surpassing last year’s submission. This year we submit three total unconstrained-only systems of which our best (contrastive 2) system uses last year’s best performing pre-trained language (PLM) model for ST (without cascading) and the inclusion of additional Quechua–Collao speech transcriptions found online. Fine-tuning of Microsoft’s SpeechT5 model in a ST setting along with the addition of new data and a data augmentation technique allowed us to achieve 26.7 BLEU. In this article, we present the three submissions along with a detailed description of the updated machine translation system where a comparison is done between synthetic, unconstrained, and other data for fine-tuning.
BUINUS at IWSLT: Evaluating the Impact of Data Augmentation and QLoRA-based Fine-Tuning for Maltese to English Speech Translation
Authors: Filbert Aurelian Tjiaranata; Vallerie Alexandra Putra; Eryawan Presma Yulianrifat; Ikhlasul Akmal Hanif
Venue: ACL 2025 (Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025))

Abstract: This paper investigates approaches for the IWSLT low-resource track, Track 1 (speech-to-text translation) for the Maltese language, focusing on data augmentation and large pre-trained models. Our system combines Whisper for transcription and NLLB for translation, with experiments concentrated mainly on the translation stage. We observe that data augmentation leads to only marginal improvements, primarily for the smaller 600M model, with gains up to 0.0026 COMET points. These gains do not extend to larger models like the 3.3B NLLB, and the overall impact appears somewhat inconsistent. In contrast, fine-tuning larger models using QLoRA outperforms full fine-tuning of smaller models. Moreover, multi-stage fine-tuning consistently improves task-specific performance across all model sizes.
LIA and ELYADATA systems for the IWSLT 2025 low-resource speech translation shared task
Authors: Chaimae Chellaf; Haroun Elleuch; Othman Istaiteh; D. Fortuné KPONOU; Fethi Bougares; Yannick Estève; Salima Mdhaffar
Venue: ACL 2025 (Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025))

Abstract: In this paper, we present the approach and system setup of our participation in the IWSLT 2025 low-resource speech translation shared task. We submitted systems for three language pairs, namely Tunisian Arabic to English, North Levantine Arabic to English, and Fongbé to French. Both pipeline and end-to-end speech translation systems were explored for Tunisian Arabic to English and Fongbé to French pairs. However, only pipeline approaches were investigated for the North Levantine Arabic–English translation direction. All our submissions are based on the usage of pre-trained models that we further fine-tune with the shared task training data.