  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.
