  In automatic speech translation (ST), traditional cascade approaches
involving separate transcription and translation steps are giving ground to
increasingly competitive and more robust direct solutions. In particular, by
translating speech audio data without intermediate transcription, direct ST
models are able to leverage and preserve essential information present in the
input (e.g. speaker's vocal characteristics) that is otherwise lost in the
cascade framework. Although such ability proved to be useful for gender
translation, direct ST is nonetheless affected by gender bias just like its
cascade counterpart, as well as machine translation and numerous other natural
language processing applications. Moreover, direct ST systems that exclusively
rely on vocal biometric features as a gender cue can be unsuitable and
potentially harmful for certain users. Going beyond speech signals, in this
paper we compare different approaches to inform direct ST models about the
speaker's gender and test their ability to handle gender translation from
English into Italian and French. To this aim, we manually annotated large
datasets with speakers' gender information and used them for experiments
reflecting different possible real-world scenarios. Our results show that
gender-aware direct ST solutions can significantly outperform strong - but
gender-unaware - direct ST models. In particular, the translation of
gender-marked words can increase up to 30 points in accuracy while preserving
overall translation quality.
