SYSTRAN @ IWSLT 2025 Low-resource track (2025)

Abstract: SYSTRAN submitted systems for one language pair in the 2025 Low-Resource Language Track. Our main contribution lies in the tight coupling and light fine-tuning of an ASR encoder (Whisper) with a neural machine translation decoder (NLLB), forming an efficient speech translation pipeline. We present the modeling strategies and optimizations implemented to build a system that, unlike large-scale end-to-end models, performs effectively under constraints of limited training data and computational resources. This approach enables the development of high-quality speech translation in low-resource settings, while ensuring both efficiency and scalability. We also conduct a comparative analysis of our proposed system against various paradigms, including a cascaded Whisper+NLLB setup and direct end-to-end fine-tuning of Whisper.

## introduction the goal of the iwslt'2025 low-resource shared task is to benchmark and promote speech translation technology for a diverse range of dialects and low-resource languages. while significant research progress has been demonstrated recently on popular datasets, many of the world's dialects and lowresource languages lack the parallel data at scale needed for standard supervised learning. thus, this share task requires creative approaches in leveraging disparate resources. the low-resource shared task will involve two tracks: systran participates exclusively in the "traditional" tunisian arabic-to-english speech transla-tion track. our system employs a tightly coupled architecture wherein the automatic speech recognition (asr) encoder directly interfaces with the neural machine translation (nmt) encoder-decoder module. this end-to-end pipeline has demonstrated robust performance in prior evaluations under lowresource conditions. the primary objective is to build a high-performance speech-to-text translation (s2tt) system optimized for constrained computational environments and limited annotated data, while effectively leveraging the representational power of large-scale pretrained models. in section 2, we describe the corpora used in this study, as well as the pre-processing steps applied to improve their relevance and quality for the target tasks (see section 3). section 4 introduces the proposed system, which combines a speech encoder with neural machine translation components. section 5 presents the experimental setup and reports the results obtained. finally, section 6 summarizes the main findings and concludes the paper. ## dataset description this work is conducted as part of a shared task aimed at advancing the state of the art in asr and speech s2tt for low-resource dialects, with a particular focus on tunisian arabic. to ensure comparability and fairness, all experiments are conducted under the constrained condition, using exclusively the tunisian-english resources provided by the linguistic data consortium (ldc) for this challenge. ## coupling whisper and nllb this work introduces a hybrid solution designed for parameter-efficient training in low-resource language scenarios inspired by the integration strategy presented in @xcite , integrating speech representation features from a pre-trained speech model into a multilingual nmt system. our approach integrates speech representation features from a pre-trained speech model encoder such as whisper into a multilingual neural machine translation system such as nllb, enabling both asr and s2tt capabilities. ## conclusions and further work we presented systran's submitted systems for the 2025 low-resource language track, targeting the task of tunisian arabic to english speech translation. our approach combines an asr encoder (whisper) with a neural machine translation decoder (nllb), using light fine-tuning to create an efficient and compact speech translation pipeline. the resulting speech-to-text translation system is designed to operate with minimal computational resources and limited training data. we evaluated our system against several alternative configurations, including a cascaded whisper+nllb setup and direct end-to-end fine-tuning of whisper. our results demonstrate that it is possible to achieve high translation quality under low-resource constraints, enabling broader accessibility without the need for large-scale infrastructure.