mlsanityData to 7 Oct 2026

Finding A Voice: Exploring the Potential of African American Dialect and Voice Generation for Chatbots

arXiv 2501.03441 ↗First posted 7 Jan 2025cs.CLcited by 1

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Abstract (arXiv)

As chatbots become integral to daily life, personalizing systems is key for fostering trust, engagement, and inclusivity. This study examines how linguistic similarity affects chatbot performance, focusing on integrating African American English (AAE) into virtual agents to better serve the African American community. We develop text-based and spoken chatbots using large language models and text-to-speech technology, then evaluate them with AAE speakers against standard English chatbots. Our results show that while text-based AAE chatbots often underperform, spoken chatbots benefit from an African American voice and AAE elements, improving performance and preference. These findings underscore the complexities of linguistic personalization and the dynamics between text and speech modalities, highlighting technological limitations that affect chatbots' AA speech generation and pointing to promising future research directions.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 20 of its 62 references that are in the map. First posted on arXiv 7 Jan 2025.

Builds on

20 of 62 references.

References · 20

  1. 2024The Llama 3 Herd of Modelscited by 14,192
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8 more
  1. 2024Can LLM Generate Culturally Relevant Commonsense QA Data? Case Study in Indonesian and Sundanesecited by 36
  2. 2024Stereotype or Personalization? User Identity Biases Chatbot Recommendationscited by 33
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  8. 2024Empowering Communication: Speech Technology for Indian and Western Accents through AI-powered Speech Synthesiscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026DialectLLM: A Dialect-Aware Dialog[ue] Generation Framework Beyond Standard American Englishcited by 0