KIT ’s Low-resource Speech Translation Systems for IWSLT 2025: System Enhancement with Synthetic Data and Model Regularization (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.

## introduction in this paper, we present our submissions to the iwslt 2025 low-resource track. we participate in three language pairs, translating from bemba (iso: bem), north levantine arabic (iso: apc), and tunisian arabic (iso: aeb) into english. our approach follows the unconstrained track, reflecting practical scenarios by leveraging all available resources, including multilingual pre-trained models and external datasets. building upon the submissions of last year @xcite , which investigates efficient utilization of available resources using multilingual pretrained models, this work explores two approaches to further enhance model performance without involving extra resources: synthetic data augmentation and model regularization. one of the main challenges in building speech translation (st) systems is the scarcity of end-toend (e2e) st data. given that automatic speech recognition (asr) and machine translation (mt) resources are more accessible, we leverage them to create synthetic st data. first, we investigate the mt-augmented approach, using a trained mt model to generate target-language translations from asr datasets. additionally, inspired by prior work @xcite @xcite @xcite , we explore synthetic speech generation. specifically, we train text-to-speech (tts) models using asr data and use them to generate synthesized speech from the mt datasets. we also explore model regularization to enhance model performance. previous research shows st systems for low-resource languages benefit from model regularization during training because of the imbalanced parameter usage @xcite . however, these works are limited to mt models in the cascaded system. since model regularization is a generic approach, this work investigates its effectiveness with both asr, mt, and st tasks. with experimental results across different language pairs, we conclude the findings as follows: the iwslt 2025 low-resource track defines two system categories: constrained, where models are trained exclusively on datasets provided by the organizers, and unconstrained, where participants are free to use any external resources. in this work, we focus on the unconstrained condition, aiming to reflect better practical and real-world scenarios, where leveraging diverse data sources is often essential for building effective translation systems. ## conclusion we participate in the iwslt 2025 low-resource track, focusing on three language pairs with bemba, north levantine, and tunisian as source languages, and english as the target language. our focus is on improving model performance through synthetic data augmentation and model regularization. the results demonstrate that high-quality synthetic data can significantly enhance performance. in addition, model regularization proves to be a robust and broadly effective approach across all asr, mt, and st tasks in low-resource settings. finally, our findings highlight the importance of language-specific strategies for building effective speech translation systems, as reflected in the varying outcomes observed across the three language pairs. mcd wer bemba vits same speaker 5.4 51.0 e2tts same speaker 5.6 40.9 e2tts cross speaker 7.7 41.9 north levantine e2tts same speaker 4.2 113.3 e2tts cross speaker 9.0 108. 3 additionally, since mcd is not a speakerindependent metric like wer, to reduce the influence of speaker attributes, we conducted assessments in both same-speaker (reconstruction) and cross-speaker settings. the results in for wer evaluation we use two asr models trained without the augmented tts data. specifically, we use model a5 from as for low-resource language north levantine, the wer scores are considerably high, suggesting that the e2tts model remains underdeveloped. this likely contributes to the poor performance of st models trained with tts-augmented data, as indicated in