A Study on the Integration of Pre-trained SSL, ASR, LM and SLU Models for Spoken Language Understanding

Yifan Peng; Siddhant Arora; Yosuke Higuchi; Yushi Ueda; Sujay Kumar; Karthik Ganesan; Siddharth Dalmia; Xuankai Chang; Shinji Watanabe

arXiv · 2211.05869

A Study on the Integration of Pre-trained SSL, ASR, LM and SLU Models for Spoken Language Understanding

Yifan Peng·Siddhant Arora·Yosuke Higuchi·Yushi Ueda·Sujay Kumar·Karthik Ganesan·Siddharth Dalmia·Xuankai Chang·Shinji Watanabe

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Abstract

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 representations. We also explore using supervised models pre-trained on larger external automatic speech recognition (ASR) or SLU corpora. We conduct extensive experiments on the SLU Evaluation (SLUE) benchmark and observe self-supervised pre-trained models to be more powerful, with pre-trained LM and speech models being most beneficial for the Sentiment Analysis and Named Entity Recognition task, respectively.

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Yifan Peng, Siddhant Arora, Yosuke Higuchi, Yushi Ueda, Sujay Kumar, Karthik Ganesan, Siddharth Dalmia, Xuankai Chang, Shinji Watanabe. 2022-11-10. A Study on the Integration of Pre-trained SSL, ASR, LM and SLU Models for Spoken Language Understanding. https://arxiv.org/abs/2211.05869

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