Finetuning End-to-End Models for Estonian Conversational Spoken Language Translation
Year: 2024

This paper investigates the finetuning of end-toend models for bidirectional Estonian-English and Estonian-Russian conversational speech-totext 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 syn