End-to-end Automatic Speech Recognition and Speech Translation: Integration of Speech Foundational Models and LLMs

• URL: http://arxiv.org/abs/2510.10329v1
• Date: Sat, 11 Oct 2025 20:10:30 GMT
• Title: End-to-end Automatic Speech Recognition and Speech Translation: Integration of Speech Foundational Models and LLMs
• Authors: Nam Luu, Ondřej Bojar,
• Abstract summary: Speech Translation (ST) is a machine translation task that involves converting speech signals from one language to the corresponding text in another language.<n>This paper explores a combined end-to-end architecture of pre-trained speech encoders and Large Language Models (LLMs) for performing both Automatic Speech Recognition (ASR) and ST simultaneously.
• Score: 0.3867363075280544
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Speech Translation (ST) is a machine translation task that involves converting speech signals from one language to the corresponding text in another language; this task has two different approaches, namely the traditional cascade and the more recent end-to-end. This paper explores a combined end-to-end architecture of pre-trained speech encoders and Large Language Models (LLMs) for performing both Automatic Speech Recognition (ASR) and ST simultaneously. Experiments with the English-to-German language pair show that our best model not only can achieve better translation results than SeamlessM4T, a large foundational end-to-end, multi-modal translation model, but can also match the performance of a cascaded system with Whisper and NLLB, with up to a score gain of 8% in $\text{COMET}^{\text{DA}}_{22}$ metric.

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