Speech Translation with Speech Foundation Models and Large Language
  Models: What is There and What is Missing?
Marco Gaido Sara Papi Matteo Negri Luisa Bentivogli
The field of natural language processing (NLP) has recently witnessed a
transformative shift with the emergence of foundation models, particularly
Large Language Models (LLMs) that have revolutionized text-based NLP. This
paradigm has extended to other modalities, including speech, where researchers
are actively exploring the combination of Speech Foundation Models (SFMs) and
LLMs into single, unified models capable of addressing multimodal tasks. Among
such tasks, this paper focuses on speech-to-text translation (ST). By examining
the published papers on the topic, we propose a unified view of the
architectural solutions and training strategies presented so far, highlighting
similarities and differences among them. Based on this examination, we not only
organize the lessons learned but also show how diverse settings and evaluation
approaches hinder the identification of the best-performing solution for each
architectural building block and training choice. Lastly, we outline
recommendations for future works on the topic aimed at better understanding the
strengths and weaknesses of the SFM+LLM solutions for ST.