Year: 2025
Type: conference-paper
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 largescale 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 endto-end fine-tuning of Whisper.
Authors: Marko Avila, Josep Crego
Institution: Systran (France)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4412944391