DiscreteSLU: A Large Language Model with Self-Supervised Discrete Speech Units for Spoken Language Understanding
<|reference_start|>DiscreteSLU: A Large Language Model with Self-Supervised Discrete Speech Units for Spoken Language Understanding: The integration of pre-trained text-based large language models (LLM) with speech input has enabled instruction-following capabilities for diverse speech tasks. This integration requires the use of a speech encoder, a speech adapter, and an LLM, trained on diverse tasks. We propose the use of discrete speech units (DSU), rather than continuous-valued speech encoder outputs, that are converted to the LLM token embedding space using the speech adapter. We generate DSU using a self-supervised speech encoder followed by k-means clustering. The proposed model shows robust performance on speech inputs from seen/unseen domains and instruction-following capability in spoken question answering. We also explore various types of DSU extracted from different layers of the self-supervised speech encoder, as well as Mel frequency Cepstral Coefficients (MFCC). Our findings suggest that the ASR task and datasets are not crucial in instruction-tuning for spoken question answering tasks.<|reference_end|>
@article{shon2024discreteslu:,
    title={DiscreteSLU: A Large Language Model with Self-Supervised Discrete Speech
  Units for Spoken Language Understanding},
    author={Suwon Shon, Kwangyoun Kim, Yi-Te Hsu, Prashant Sridhar, Shinji
  Watanabe, Karen Livescu},
    journal={arXiv preprint arXiv:2406.09345},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.09345},
    primaryClass={cs.CL cs.SD eess.AS}
}