When translating words referring to the speaker, speech translation (ST)
systems should not resort to default masculine generics nor rely on potentially
misleading vocal traits. Rather, they should assign gender according to the
speakers' preference. The existing solutions to do so, though effective, are
hardly feasible in practice as they involve dedicated model re-training on
gender-labeled ST data. To overcome these limitations, we propose the first
inference-time solution to control speaker-related gender inflections in ST.
Our approach partially replaces the (biased) internal language model (LM)
implicitly learned by the ST decoder with gender-specific external LMs.
Experiments on en->es/fr/it show that our solution outperforms the base models
and the best training-time mitigation strategy by up to 31.0 and 1.6 points in
gender accuracy, respectively, for feminine forms. The gains are even larger
(up to 32.0 and 3.4) in the challenging condition where speakers' vocal traits
conflict with their gender.

用于语音翻译系统的控制说话者性别变化的解决方案通过使用性别特定的外部语言模型，在性别准确性方面比基本模型和最好的训练时间缓解策略分别提高了 31.0 和 1.6 个百分点，特别是在说话者的声音特征与性别相冲突的情况下，增益甚至更大（最多 32.0 和 3.4 个百分点）。