  The impressive capability and versatility of large language models (LLMs)
have aroused increasing attention in automatic speech recognition (ASR), with
several pioneering studies attempting to build integrated ASR models by
connecting a speech encoder with an LLM. This paper presents a comparative
study of three commonly used structures as connectors, including fully
connected layers, multi-head cross-attention, and Q-Former. Speech encoders
from the Whisper model series as well as LLMs from the Vicuna model series with
different model sizes were studied. Experiments were performed on the commonly
used LibriSpeech, Common Voice, and GigaSpeech datasets, where the LLMs with
Q-Formers demonstrated consistent and considerable word error rate (WER)
reductions over LLMs with other connector structures. Q-Former-based LLMs can
generalise well to out-of-domain datasets, where 12% relative WER reductions
over the Whisper baseline ASR model were achieved on the Eval2000 test set
without using any in-domain training data from Switchboard. Moreover, a novel
segment-level Q-Former is proposed to enable LLMs to recognise speech segments
with a duration exceeding the limitation of the encoders, which results in 17%
relative WER reductions over other connector structures on 90-second-long
speech data.
