  Recent studies on direct speech translation show continuous improvements by
means of data augmentation techniques and bigger deep learning models. While
these methods are helping to close the gap between this new approach and the
more traditional cascaded one, there are many incongruities among different
studies that make it difficult to assess the state of the art. Surprisingly,
one point of discussion is the segmentation of the target text. Character-level
segmentation has been initially proposed to obtain an open vocabulary, but it
results on long sequences and long training time. Then, subword-level
segmentation became the state of the art in neural machine translation as it
produces shorter sequences that reduce the training time, while being superior
to word-level models. As such, recent works on speech translation started using
target subwords despite the initial use of characters and some recent claims of
better results at the character level. In this work, we perform an extensive
comparison of the two methods on three benchmarks covering 8 language
directions and multilingual training. Subword-level segmentation compares
favorably in all settings, outperforming its character-level counterpart in a
range of 1 to 3 BLEU points.
