生成多语言性别模糊的文本转语音语音
Generating Multilingual Gender-Ambiguous Text-to-Speech Voices
AI总结：
本研究提出一种性别感知方法，从潜在说话人嵌入空间高效采样，在多说话人多语言场景下生成性别模糊的TTS语音，经评估效果优于基线，且听众性别与母语不影响性别感知，是首个经验证的相关系统方法。
摘要：
任何语音用户界面的性别都是其感知身份的关键要素。近来，人们对性别模糊而非明确为女性或男性的界面兴趣日益浓厚。本研究针对多说话人、多语言场景下生成全新性别模糊TTS（文本转语音）语音的任务展开研究，通过提出的性别感知方法从潜在说话人嵌入空间高效采样来实现该目标。大量客观与主观评估明确表明，该方法能够高效生成一系列全新、多样的语音，在所有被测语言中，这些语音表现一致，且被感知为比基线语音更具性别模糊性。有趣的是，研究发现性别感知在听众的两个人口统计因素（母语和性别）上均表现稳定。据我们所知，这是首个能够可靠生成多种性别模糊语音的系统性且经过验证的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
