  In a sentence, certain words are critical for its semantic. Among them, named
entities (NEs) are notoriously challenging for neural models. Despite their
importance, their accurate handling has been neglected in speech-to-text (S2T)
translation research, and recent work has shown that S2T models perform poorly
for locations and notably person names, whose spelling is challenging unless
known in advance. In this work, we explore how to leverage dictionaries of NEs
known to likely appear in a given context to improve S2T model outputs. Our
experiments show that we can reliably detect NEs likely present in an utterance
starting from S2T encoder outputs. Indeed, we demonstrate that the current
detection quality is sufficient to improve NE accuracy in the translation with
a 31% reduction in person name errors.
