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Title: Introducing Semantics into Speech Encoders
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 taskagnostic unsupervised way of incorporating semantic information from LLMs into selfsupervised speech encoders without labeled audio transcriptions. By introducing semantics, we improve existing speech encoder spoken language understanding (SLU) performance by over 5% on intent classification (IC), with modest gains in named entity resolution (NER) and slot filling (SF), and spoken question answering (SQA) FF1 score by over 2%. Our approach, which uses no ASR data, achieves similar performance as methods trained on over 100 hours of labeled audio transcripts, demonstrating the feasibility of unsupervised semantic augmentations to existing speech encoders.  more » « less
Award ID(s):
2211557 1937599
NSF-PAR ID:
10464432
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ; ; ; ; ;
Date Published:
Journal Name:
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics
Volume:
1
Page Range / eLocation ID:
11413 to 11429
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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