  Recent research has shown that independently trained encoders and decoders,
combined through a shared fixed-size representation, can achieve competitive
performance in speech-to-text translation. In this work, we show that this type
of approach can be further improved with multilingual training. We observe
significant improvements in zero-shot cross-modal speech translation, even
outperforming a supervised approach based on XLSR for several languages.
