End-to-End Speech Recognition Contextualization with Large Language
  Models
Egor Lakomkin Chunyang Wu Yassir Fathullah Ozlem Kalinli Michael L. Seltzer Christian Fuegen
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.