Bridging Modality Gap with Large Speech and Language Models for End-to-End Speech-to-Text Translation

End-to-end speech-to-text translation (E2E ST) has increasingly aroused interest and attention recently, attempting to address the problem of data scarcity and modeling burden. Several attempts exploring the combination of Large Speech and Language Models into a unified model to improve E2E ST are carried out. However, the inherent differences between speech and text modalities often impede effective cross-modal and cross-lingual transfer. In this study, we introduce LaSaLM-ST, a novel model architecture built upon Pre-trained Large Speech and Language Models for improving E2E ST. Our speech encoder begins with processing the source speech sequence. An adaptor and speech decoder then project speech features into the compatible feature space for the decoder-only Large Language Model (LLM), which then aligns the representation spaces of speech and text modalities with attentive interactions. Besides, we also develop a multi-step fine-tuning method to preserve the pre-trained multilingual knowledge and keep ST fine-tuning stably. Experiments conducted on the IWSLT2023 offline ST task from English to German, Chinese and Japanese demonstrate that our methodology not only achieves state-of-the-art BLEU scores but also outperforms the highly competitive cascaded ST systems in an unrestricted setting.