  Conventional speech-to-text translation (ST) systems are trained on
single-speaker utterances, and they may not generalize to real-life scenarios
where the audio contains conversations by multiple speakers. In this paper, we
tackle single-channel multi-speaker conversational ST with an end-to-end and
multi-task training model, named Speaker-Turn Aware Conversational Speech
Translation, that combines automatic speech recognition, speech translation and
speaker turn detection using special tokens in a serialized labeling format. We
run experiments on the Fisher-CALLHOME corpus, which we adapted by merging the
two single-speaker channels into one multi-speaker channel, thus representing
the more realistic and challenging scenario with multi-speaker turns and
cross-talk. Experimental results across single- and multi-speaker conditions
and against conventional ST systems, show that our model outperforms the
reference systems on the multi-speaker condition, while attaining comparable
performance on the single-speaker condition. We release scripts for data
processing and model training.
