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

• URL: http://arxiv.org/abs/2211.05869v1
• Date: Thu, 10 Nov 2022 20:59:13 GMT
• Title: A Study on the Integration of Pre-trained SSL, ASR, LM and SLU Models for Spoken Language Understanding
• Authors: Yifan Peng, Siddhant Arora, Yosuke Higuchi, Yushi Ueda, Sujay Kumar, Karthik Ganesan, Siddharth Dalmia, Xuankai Chang, Shinji Watanabe
• Abstract summary: We employ four types of pre-trained models and their combinations for spoken language understanding (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.
• Score: 42.345266746904514
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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