Finetuning End-to-End Models for Estonian Conversational Spoken Language Translation
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Abstract (arXiv)
This paper investigates the finetuning of end-to-end models for bidirectional Estonian-English and Estonian-Russian conversational speech-to-text translation. Due to the limited availability of speech translation data for Estonian, we created additional training data by web scraping and synthesizing data from speech recognition datasets using machine translation. We evaluated three publicly available end-to-end models: Whisper, OWSM 3.1, and SeamlessM4T. Our results indicate that fine-tuning with synthetic data enhances translation accuracy by a large margin, with SeamlessM4T matching or surpassing cascaded speech translation systems that use state-of-the-art speech recognition and machine translation models.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 16 of its 22 references that are in the map. First posted on arXiv 4 Jul 2024.
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4 more
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