DiscreteSLU: A Large Language Model with Self-Supervised Discrete Speech Units for Spoken Language Understanding
Suwon Shon, Kwangyoun Kim, Yi‐Te Hsu, Prashant Sridhar, Shinji Watanabe, Karen Livescu · 2024
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 selfsupervised 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.