Comparing Discrete and Continuous Space LLMs for Speech Recognition

• URL: http://arxiv.org/abs/2409.00800v1
• Date: Sun, 1 Sep 2024 18:29:45 GMT
• Title: Comparing Discrete and Continuous Space LLMs for Speech Recognition
• Authors: Yaoxun Xu, Shi-Xiong Zhang, Jianwei Yu, Zhiyong Wu, Dong Yu,
• Abstract summary: This paper investigates discrete and continuous speech representations in Large Language Model (LLM)-based Automatic Speech Recognition (ASR) We classify LLMs based on their input and autoregressive feedback into continuous and discrete-space models. 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.
• Score: 46.70297458685438
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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