End-to-End Speech Recognition Contextualization with Large Language Models

Egor Lakomkin; Chunyang Wu; Yassir Fathullah; Ozlem Kalinli; Michael L. Seltzer; Christian Fuegen

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arXiv · 2309.10917

End-to-End Speech Recognition Contextualization with Large Language Models

Egor Lakomkin·Chunyang Wu·Yassir Fathullah·Ozlem Kalinli·Michael L. Seltzer·Christian Fuegen

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Abstract

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.

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eess.AS cs.AI cs.CL cs.LG cs.SD

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BibTeX RIS

Egor Lakomkin, Chunyang Wu, Yassir Fathullah, Ozlem Kalinli, Michael L. Seltzer, Christian Fuegen. 2023-09-19. End-to-End Speech Recognition Contextualization with Large Language Models. https://arxiv.org/abs/2309.10917

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