LIA and ELYADATA systems for the IWSLT 2025 low-resource speech translation shared task (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.

## introduction the international workshop on spoken language translation (iwslt) is an annual scientific conference dedicated to the study and advancement of spoken language translation technologies. it serves as a platform for researchers and practitioners to present their work on speech translation, encompassing areas such as automatic speech recognition (asr) and machine translation (mt). iwslt has played a pivotal role in the advancement of spoken language translation (st) by providing a structured environment to evaluate and compare different approaches. its emphasis on real-world challenges, such as low-resource languages and real-time translation, has contributed to the development of more robust and versatile translation systems. iwslt 2025 @xcite proposes two shared tasks: high-resource st and low-resource st. several language pairs were proposed this year for the low-resource task. in this paper, we focus on tunisian arabic-english, north levantine-english and fongbé-french language pairs. this paper describes the approach and sys-* these authors contributed equally to this work tem setup of the joint participation of lia and elyadata in the tasks as mentioned earlier. for the tunisian arabic-english and fongbé-french tracks, both end-to-end (e2e) and pipeline approaches were explored. in contrast, only pipeline approaches were investigated for the north levantine arabic-english track. for e2e approaches, we focus on fine-tuning selfsupervised learning (ssl) models and whisper models @xcite . all systems are trained with an unconstrained setup, which means that any resource, including pre-trained language models, can be used, except for evaluation sets. we particularly investigate the samu-like approach @xcite to enrich the ssl speech encoder with semantic information. for pipeline approaches, we focus on fine-tuning large language models (llms). the remaining of the paper is structured as follows: section 2 presents the related work. section 3 is dedicated to describe our systems for tunisian arabic to english. the experiments for fongbé to french and for north levantine to english are presented, respectively, in sections 4 and 5. section 6 concludes the paper and discusses future work. ## related work the speech translation task has received considerable attention from the research community, and numerous approaches have been proposed. traditional speech translation (st) approaches follow a cascade architecture @xcite @xcite , where an automatic speech recognition (asr) system is followed by a machine translation (mt) module applied to the asr output. recent advances in deep neural networks for both asr and mt have led to substantial improvements in the overall performance of st systems. more recently, end-to-end speech translation models @xcite @xcite have gained attention as an alternative to the traditional cascade architecture. these models aim to directly translate speech in a source language into text or speech in the target language without requiring intermediate text transcriptions. end-to-end models reduce latency, avoid error propagation between asr and mt components, and can be optimized globally for the final translation objective. with the emergence of robust transformer-based architectures and multilingual pretraining methods, such as those used in seamlessm4t (seamless @xcite , speech translation systems have gained momentum, leading to diversity in model architectures and training methods. meta's seamlessm4t stands out as a unified multimodal system capable of handling speech-totext translation across 101 input and 96 output languages. openai's whisper @xcite is an automatic speech recognition (asr) system that also offers speech-to-text translation capabilities. trained on 680,000 hours of multilingual and multitask supervised data, whisper demonstrates robustness to accents, background noise, and technical language. it supports transcription in multiple languages and translation from those languages into english. of the 680,000 hours of labelled audio used by whisper, 117,000 hours cover 96 other languages. the dataset also includes 125,000 hours of @xmath0. beyond whisper and seamlessm4t, several other models have emerged that employ self-supervised learning (ssl) to enhance performance in speech translation. wav2vec 2.0 @xcite , introduced by facebook ai, is one of the earliest ssl-based models that significantly improved asr performance. wav2vec 2.0 is typically coupled with a transformer decoder for speech translation. building on this foundation, w2v-bert @xcite and hubert @xcite models have been developed. in this paper, we investigate these recent advances in speech-to-text translation systems to participate in the iwslt low-resource speech translation shared task. ## conclusion this paper describes the translation systems developed by lia and elyadata for three tracks of the iwslt 2025 evaluation campaign, focusing on low-resource speech translation. the targeted language pairs are tunisian arabic-english, north levantine arabic-english, and fongbé-french.