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.
@article{arxiv.2406.09345,
title = {DiscreteSLU: A Large Language Model with Self-Supervised Discrete Speech Units for Spoken Language Understanding},
author = {Suwon Shon and Kwangyoun Kim and Yi-Te Hsu and Prashant Sridhar and Shinji Watanabe and Karen Livescu},
journal= {arXiv preprint arXiv:2406.09345},
year = {2024}
}