  Advances in machine learning have made it possible to perform various text
and speech processing tasks, such as automatic speech recognition (ASR), in an
end-to-end (E2E) manner. E2E approaches utilizing pre-trained models are
gaining attention for conserving training data and resources. However, most of
their applications in ASR involve only one of either a pre-trained speech or a
language model. This paper proposes integrating a pre-trained speech
representation model and a large language model (LLM) for E2E ASR. The proposed
model enables the optimization of the entire ASR process, including acoustic
feature extraction and acoustic and language modeling, by combining pre-trained
models with a bridge network and also enables the application of remarkable
developments in LLM utilization, such as parameter-efficient domain adaptation
and inference optimization. Experimental results demonstrate that the proposed
model achieves a performance comparable to that of modern E2E ASR models by
utilizing powerful pre-training models with the proposed integrated approach.
