Discrete Multimodal Transformers with a Pretrained Large Language Model for Mixed-Supervision Speech Processing
<|reference_start|>Discrete Multimodal Transformers with a Pretrained Large Language Model for Mixed-Supervision Speech Processing: Recent work on discrete speech tokenization has paved the way for models that can seamlessly perform multiple tasks across modalities, e.g., speech recognition, text to speech, speech to speech translation. Moreover, large language models (LLMs) pretrained from vast text corpora contain rich linguistic information that can improve accuracy in a variety of tasks. In this paper, we present a decoder-only Discrete Multimodal Language Model (DMLM), which can be flexibly applied to multiple tasks (ASR, T2S, S2TT, etc.) and modalities (text, speech, vision). We explore several critical aspects of discrete multi-modal models, including the loss function, weight initialization, mixed training supervision, and codebook. Our results show that DMLM benefits significantly, across multiple tasks and datasets, from a combination of supervised and unsupervised training. Moreover, for ASR, it benefits from initializing DMLM from a pretrained LLM, and from a codebook derived from Whisper activations.<|reference_end|>
@article{trinh2024discrete,
    title={Discrete Multimodal Transformers with a Pretrained Large Language Model
  for Mixed-Supervision Speech Processing},
    author={Viet Anh Trinh, Rosy Southwell, Yiwen Guan, Xinlu He, Zhiyong Wang,
  Jacob Whitehill},
    journal={arXiv preprint arXiv:2406.06582},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.06582},
    primaryClass={cs.CL cs.LG eess.AS}
}