  Recent work has shown that systems for speech translation (ST) -- similarly
to automatic speech recognition (ASR) -- poorly handle person names. This
shortcoming does not only lead to errors that can seriously distort the meaning
of the input, but also hinders the adoption of such systems in application
scenarios (like computer-assisted interpreting) where the translation of named
entities, like person names, is crucial. In this paper, we first analyse the
outputs of ASR/ST systems to identify the reasons of failures in person name
transcription/translation. Besides the frequency in the training data, we
pinpoint the nationality of the referred person as a key factor. We then
mitigate the problem by creating multilingual models, and further improve our
ST systems by forcing them to jointly generate transcripts and translations,
prioritising the former over the latter. Overall, our solutions result in a
relative improvement in token-level person name accuracy by 47.8% on average
for three language pairs (en->es,fr,it).
