Representations of AI agents in user interfaces and robotics are predominantly White, not only in terms of facial and skin features, but also in the synthetic voices they use. In this paper we explore some unexpected challenges in the representation of race we found in the process of developing an U.S. English Text-to-Speech (TTS) system aimed to sound like an educated, professional, regional accent-free African American woman. The paper starts by presenting the results of focus groups with African American IT professionals where guidelines and challenges for the creation of a representative and appropriate TTS system were discussed and gathered, followed by a discussion about some of the technical difficulties faced by the TTS system developers. We then describe two studies with U.S. English speakers where the participants were not able to attribute the correct race to the African American TTS voice while overwhelmingly correctly recognizing the race of a White TTS system of similar quality. A focus group with African American IT workers not only confirmed the representativeness of the African American voice we built, but also suggested that the surprising recognition results may have been caused by the inability or the latent prejudice from non-African Americans to associate educated, non-vernacular, professionally-sounding voices to African American people.

AI机器人和用户界面的表示普遍是白人，无论是面部和肤色特征上，还是合成的声音；本文探讨了在开发一种以美国非洲裔女性的受教育水平高、专业、不带地方口音的英文文字转语音系统时，所面临的对种族的表示所带来的一些意外挑战，研究开始通过针对非洲裔美国的IT专业人员的焦点小组讨论和收集指南和挑战，随后讨论了文字转语音系统开发者面临的技术困难，接着描述了两个研究，参与者在这些研究中无法正确地将非洲裔美国人的声音归因于非洲裔美国人的文字转语音系统，但在类似质量的白人文字转语音系统中却能够正确地识别到种族，该研究进一步验证了我们所构建的非洲裔美国人声音的代表性，并表明非洲裔美国人对于非白人的教育水平高、非方言、专业化的声音与非洲裔美国人的关联可能受到非非洲裔美国人的无能或潜在偏见的影响。

打造具有非洲裔美国人口音的文本转语音系统：指导原则、技术挑战和令人惊讶的评估