Creating an African American-Sounding TTS: Guidelines, Technical
  Challenges,and Surprising Evaluations
Claudio Pinhanez Raul Fernandez Marcelo Grave Julio Nogima Ron Hoory
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.