GMU Systems for the IWSLT 2025 Low-Resource Speech Translation Shared Task
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| Format: | Preprint |
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2025
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| _version_ | 1866912399279259648 |
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| author | Meng, Chutong Anastasopoulos, Antonios |
| author_facet | Meng, Chutong Anastasopoulos, Antonios |
| contents | 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 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_21781 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GMU Systems for the IWSLT 2025 Low-Resource Speech Translation Shared Task Meng, Chutong Anastasopoulos, Antonios Computation and Language 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 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. |
| title | GMU Systems for the IWSLT 2025 Low-Resource Speech Translation Shared Task |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2505.21781 |