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arXiv · 2409.00800

Comparing Discrete and Continuous Space LLMs for Speech Recognition

Yaoxun Xu·Shi-Xiong Zhang·Jianwei Yu·Zhiyong Wu·Dong Yu
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Abstract

This paper investigates discrete and continuous speech representations in Large Language Model (LLM)-based Automatic Speech Recognition (ASR), organizing them by feature continuity and training approach into four categories: supervised and unsupervised for both discrete and continuous types. We further classify LLMs based on their input and autoregressive feedback into continuous and discrete-space models. Using specialized encoders and comparative analysis with a Joint-Training-From-Scratch Language Model (JTFS LM) and pre-trained LLaMA2-7b, we provide a detailed examination of their effectiveness. Our work marks the first extensive comparison of speech representations in LLM-based ASR and explores various modeling techniques. We present an open-sourced achievement of a state-of-the-art Word Error Rate (WER) of 1.69\% on LibriSpeech using a HuBERT encoder, offering valuable insights for advancing ASR and natural language processing (NLP) research.

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Yaoxun Xu, Shi-Xiong Zhang, Jianwei Yu, Zhiyong Wu, Dong Yu. 2024-09-01. Comparing Discrete and Continuous Space LLMs for Speech Recognition. https://arxiv.org/abs/2409.00800

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