  When building state-of-the-art speech translation models, the need for large
computational resources is a significant obstacle due to the large training
data size and complex models. The availability of pre-trained models is a
promising opportunity to build strong speech translation systems efficiently.
In a first step, we investigate efficient strategies to build cascaded and
end-to-end speech translation systems based on pre-trained models. Using this
strategy, we can train and apply the models on a single GPU. While the
end-to-end models show superior translation performance to cascaded ones, the
application of this technology has a limitation on the need for additional
end-to-end training data. In a second step, we proposed an additional
similarity loss to encourage the model to generate similar hidden
representations for speech and transcript. Using this technique, we can
increase the data efficiency and improve the translation quality by 6 BLEU
points in scenarios with limited end-to-end training data.
