Analyzing the Creation of an African American-Sounding TTS System

Core Concepts

Developing a TTS system to sound like an African American voice poses challenges and reveals biases in recognition.

Abstract

The paper explores the creation of an African American-sounding TTS system, highlighting challenges faced in representing race. It discusses focus groups with African American IT professionals to gather guidelines for developing the voice. Technical difficulties in capturing African American voices are described, along with studies showing participants' inability to recognize the AA voice as such. The study aims to address misconceptions and prejudices affecting the evaluation of synthetic voices based on race.

Stats

Participants were not able to attribute correct race to the African American TTS voice. Studies showed U.S. English speakers struggled to recognize the AA voice as African American. Focus groups highlighted ethical considerations and selection criteria for developing an authentic AA voice. Technical quality evaluation of the AA voice was not addressed in the paper.

Quotes

"Participants were unable to distinguish between White and African American synthetic voices." "African Americans confirmed representativeness of the created voice but suggested recognition issues were due to misconceptions." "The study revealed challenges in creating a synthetic voice that accurately represents an African American speaker."

Key Insights Distilled From

Creating an African American-Sounding TTS

by Claudio Pinh... at arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.11209.pdf

Deeper Inquiries

What implications do these findings have for diversity and inclusivity in technology

The findings from the studies have significant implications for diversity and inclusivity in technology. The fact that U.S. English speakers were unable to correctly attribute the African American synthetic voice to an African American person highlights a lack of representation and recognition of diverse voices in technology. This can perpetuate existing biases and reinforce stereotypes, ultimately hindering efforts towards creating inclusive technologies. It underscores the importance of ensuring that diverse voices are accurately represented and recognized in AI systems to promote diversity and inclusivity.

How can biases in recognizing synthetic voices be mitigated or addressed

Biases in recognizing synthetic voices can be mitigated or addressed through various strategies. One approach is to increase diversity in training data used for developing TTS systems, ensuring that a wide range of voices, including those from underrepresented groups, are included. Additionally, implementing bias detection algorithms during model training and evaluation can help identify and mitigate any biases present in the system. Providing transparency about the data sources used and the processes involved in developing synthetic voices can also help address biases by promoting accountability and ethical considerations.

How might cultural perceptions influence the development and reception of AI technologies

Cultural perceptions play a significant role in both the development and reception of AI technologies. Developers must consider cultural nuances, beliefs, values, and preferences when designing AI systems to ensure they resonate with diverse user populations. Understanding how different cultures perceive technology, language use, accents, dialects, and representation is crucial for creating inclusive AI solutions that cater to a global audience. By incorporating cultural sensitivity into design processes and engaging with diverse communities throughout development stages, developers can create more culturally responsive AI technologies that better serve users worldwide.