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.

本论文研究语义对齐的语音表示在端到端语音理解中的应用，并使用 SAMU-XLSR 模型，在不同语言中捕捉语音的语义，将语音表现学习模型（XLS-R）与语言无关的 BERT 句子嵌入（LaBSE）模型相结合。我们证明该模型的使用显著提高了端到端 SLU 的性能，并展示了在 SLU 中使用该模型的好处，特别是语言易携带性。