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In Transformer-based Speech-to-Text (S2T) translation, an encoder-decoder model is trained end-to-end to take as input an untranscribed acoustic signal in the source language and directly generate a text translation in the target language. S2T translation models can also be trained in multilingual setups where a single front-end speech encoder is shared across multiple languages. A lingering question, however, is whether the encoder represents spoken utterances in a language-neutral space. In this paper, we present an interpretability study of encoder representations in a multilingual speech translation Transformer via various probing tasks. Our main findings show that while encoder representations are not entirely language-neutral, there exists a semantic subspace that is shared across different languages. Furthermore, we discuss our findings and the implication of our study on cross-lingual learning for spoken language understanding tasks.