The gender of any voice user interface is a key element of its perceived identity. Recently, there has been increasing interest in interfaces where the gender is ambiguous rather than clearly identifying as female or male. This work addresses the task of generating novel gender-ambiguous TTS voices in a multi-speaker, multilingual setting. This is accomplished by efficiently sampling from a latent speaker embedding space using a proposed gender-aware method. Extensive objective and subjective evaluations clearly indicate that this method is able to efficiently generate a range of novel, diverse voices that are consistent and perceived as more gender-ambiguous than a baseline voice across all the languages examined. Interestingly, the gender perception is found to be robust across two demographic factors of the listeners: native language and gender. To our knowledge, this is the first systematic and validated approach that can reliably generate a variety of gender-ambiguous voices.

翻译：任何语音用户界面的性别都是其感知身份的关键要素。近年来，人们对性别模糊（而非明确识别为女性或男性）的界面兴趣日益增长。本研究旨在解决在多说话人多语言环境中生成新型性别模糊TTS语音的任务。通过提出一种性别感知方法，从潜在说话人嵌入空间中高效采样实现该目标。大量客观与主观评估明确表明，该方法能高效生成一系列新颖多样的语音，这些语音在所有考察语言中均表现出一致性，且相比基线语音被感知为更模糊性别特征。值得注意的是，性别感知在听者的两个人口统计学因素（母语与性别）中均具有稳健性。据我们所知，这是首个经过系统性验证并能可靠生成多种性别模糊语音的方法。