  Large Language Models (LLMs) have demonstrated unparalleled effectiveness in
various NLP tasks, and integrating LLMs with automatic speech recognition (ASR)
is becoming a mainstream paradigm. Building upon this momentum, our research
delves into an in-depth examination of this paradigm on a large open-source
Chinese dataset. Specifically, our research aims to evaluate the impact of
various configurations of speech encoders, LLMs, and projector modules in the
context of the speech foundation encoder-LLM ASR paradigm. Furthermore, we
introduce a three-stage training approach, expressly developed to enhance the
model's ability to align auditory and textual information. The implementation
of this approach, alongside the strategic integration of ASR components,
enabled us to achieve the SOTA performance on the AISHELL-1, Test_Net, and
Test_Meeting test sets. Our analysis presents an empirical foundation for
future research in LLM-based ASR systems and offers insights into optimizing
performance using Chinese datasets. We will publicly release all scripts used
for data preparation, training, inference, and scoring, as well as pre-trained
models and training logs to promote reproducible research.
