让聊天机器人说非裔英语，语音版更受非裔用户欢迎。
Finding A Voice: Exploring the Potential of African American Dialect and Voice Generation for Chatbots
- 用大模型和语音合成技术构建支持非裔英语的对话系统。
- 语音版聊天机器人在非裔用户中表现更好，满意度更高。
- 揭示了文本与语音模态在语言个性化中的差异与挑战。
随着聊天机器人融入日常生活，个性化对建立信任、提升参与度和促进包容性至关重要。本研究探讨语言相似性对聊天机器人性能的影响，聚焦将非裔美国英语（AAE）融入虚拟代理以更好地服务非裔美国人社区。我们利用大语言模型和文本转语音技术开发了基于文本和语音的聊天机器人，并邀请非裔英语使用者与标准英语聊天机器人进行对比评估。结果表明，虽然基于文本的非裔英语聊天机器人表现通常较差，但采用非裔美国口音和非裔英语元素的语音聊天机器人在性能和用户偏好上均有显著提升。研究凸显了语言个性化背后的复杂性以及文本与语音模态间的动态关系，指出了当前技术在生成非裔英语语音方面的局限，并为未来研究提供了重要方向。
原文摘要 · Abstract (English)
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.
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