Introducing Semantics into Speech Encoders

• URL: http://arxiv.org/abs/2211.08402v1
• Date: Tue, 15 Nov 2022 18:44:28 GMT
• Title: Introducing Semantics into Speech Encoders
• Authors: Derek Xu, Shuyan Dong, Changhan Wang, Suyoun Kim, Zhaojiang Lin, Akshat Shrivastava, Shang-Wen Li, Liang-Hsuan Tseng, Alexei Baevski, Guan-Ting Lin, Hung-yi Lee, Yizhou Sun, Wei Wang
• Abstract summary: We propose an unsupervised way of incorporating semantic information from large language models into self-supervised speech encoders without labeled audio transcriptions. Our approach achieves similar performance as supervised methods trained on over 100 hours of labeled audio transcripts.
• Score: 91.37001512418111
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent studies find existing self-supervised speech encoders contain primarily acoustic rather than semantic information. As a result, pipelined supervised automatic speech recognition (ASR) to large language model (LLM) systems achieve state-of-the-art results on semantic spoken language tasks by utilizing rich semantic representations from the LLM. These systems come at the cost of labeled audio transcriptions, which is expensive and time-consuming to obtain. We propose a task-agnostic unsupervised way of incorporating semantic information from LLMs into self-supervised speech encoders without labeled audio transcriptions. By introducing semantics, we improve existing speech encoder spoken language understanding performance by over 10\% on intent classification, with modest gains in named entity resolution and slot filling, and spoken question answering FF1 score by over 2\%. Our unsupervised approach achieves similar performance as supervised methods trained on over 100 hours of labeled audio transcripts, demonstrating the feasibility of unsupervised semantic augmentations to existing speech encoders.

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