GMU Systems for the IWSLT 2025 Low-Resource Speech Translation Shared Task

This paper describes the GMU systems for the IWSLT 2025 low-resource speech translation shared task.We trained systems for all language pairs, except for Levantine Arabic.We fine-tuned SeamlessM4T-v2 (Seamless Communication et al., 2023b) for automatic speech recognition (ASR), machine translation (MT), and end-to-end speech translation (E2E ST).The ASR and MT models are also used to form cascaded ST systems.Additionally, we explored various training paradigms for E2E ST fine-tuning, including direct E2E fine-tuning, multi-task training, and parameter initialization using components from fine-tuned ASR and/or MT models.Our results show that (1) direct E2E fine-tuning yields strong results; (2) initializing with a fine-tuned ASR encoder improves ST performance on languages SeamlessM4T-v2 has not been trained on; (3) multi-task training can be slightly helpful. 1