In recent years, Large Language Models (LLMs) have garnered significant
attention from the research community due to their exceptional performance and
generalization capabilities. In this paper, we introduce a novel method for
contextualizing speech recognition models incorporating LLMs. Our approach
casts speech recognition as a mixed-modal language modeling task based on a
pretrained LLM. We provide audio features, along with optional text tokens for
context, to train the system to complete transcriptions in a decoder-only
fashion. As a result, the system is implicitly incentivized to learn how to
leverage unstructured contextual information during training. Our empirical
results demonstrate a significant improvement in performance, with a 6% WER
reduction when additional textual context is provided. Moreover, we find that
our method performs competitively and improve by 7.5% WER overall and 17% WER
on rare words against a baseline contextualized RNN-T system that has been
trained on more than twenty five times larger speech dataset. Overall, we
demonstrate that by only adding a handful number of trainable parameters via
adapters, we can unlock contextualized speech recognition capability for the
pretrained LLM while keeping the same text-only input functionality.

通过引入一种新方法，结合大型语言模型（LLMs）来进行上下文化的语音识别模型，我们证明通过添加适配器的少量可训练参数，可以在保持相同的文本输入功能的同时，实现预训练 LLM 的上下文化语音识别能力并显著提高性能。