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On the Use of Semantically-Aligned Speech Representations for Spoken Language Understanding

2022-10-11 09:40:34
Gaëlle Laperrière, Valentin Pelloin, Mickaël Rouvier, Themos Stafylakis, Yannick Estève
arXiv_CL
         
Abstract

In this paper we examine the use of semantically-aligned speech representations for end-to-end spoken language understanding (SLU). We employ the recently-introduced SAMU-XLSR model, which is designed to generate a single embedding that captures the semantics at the utterance level, semantically aligned across different languages. This model combines the acoustic frame-level speech representation learning model (XLS-R) with the Language Agnostic BERT Sentence Embedding (LaBSE) model. We show that the use of the SAMU-XLSR model instead of the initial XLS-R model improves significantly the performance in the framework of end-to-end SLU. Finally, we present the benefits of using this model towards language portability in SLU.

Abstract (translated)

URL

https://arxiv.org/abs/2210.05291

PDF

https://arxiv.org/pdf/2210.05291.pdf

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