Nov. 14, 2022, 2:15 a.m. | Yifan Peng, Siddhant Arora, Yosuke Higuchi, Yushi Ueda, Sujay Kumar, Karthik Ganesan, Siddharth Dalmia, Xuankai Chang, Shinji Watanabe

cs.CL updates on arXiv.org arxiv.org

Collecting sufficient labeled data for spoken language understanding (SLU) is
expensive and time-consuming. Recent studies achieved promising results by
using pre-trained models in low-resource scenarios. Inspired by this, we aim to
ask: which (if any) pre-training strategies can improve performance across SLU
benchmarks? To answer this question, we employ four types of pre-trained models
and their combinations for SLU. We leverage self-supervised speech and language
models (LM) pre-trained on large quantities of unpaired data to extract strong
speech and text …

arxiv asr integration language slu spoken language understanding ssl study understanding

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