  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.
