1000种非洲声音：推进包容性多说话人多口音语音合成
1000 African Voices: Advancing inclusive multi-speaker multi-accent speech synthesis
AI总结：
针对非洲语音在语音合成系统中代表性不足的问题，提出首个泛非洲口音英语语音合成系统Afro-TTS，支持86种非洲口音和1000个角色，并验证了说话人插值创造新语音的有效性。
摘要：
语音合成的最新进展催生了许多实用应用，如Google Maps中的音频导航、屏幕阅读器以及TikTok等平台上的自动化内容生成。然而，这些系统主要由来自数据丰富地区的语音主导，其人物角色代表了其源数据。尽管世界上有3000种语言分布在非洲，但非洲的语音和人物角色在这些系统中代表性不足。随着语音合成日益普及，增加对非洲英语口音的代表性变得可取。我们提出了Afro-TTS，这是首个泛非洲口音英语语音合成系统，能够生成86种非洲口音的语音，包含1000个代表该大陆丰富语音多样性的人物角色，用于教育、公共卫生和自动化内容创作等下游应用。说话人插值保留了自然度和口音特征，从而能够创造新的语音。
英文摘要：
Recent advances in speech synthesis have enabled many useful applications like audio directions in Google Maps, screen readers, and automated content generation on platforms like TikTok. However, these systems are mostly dominated by voices sourced from data-rich geographies with personas representative of their source data. Although 3000 of the world's languages are domiciled in Africa, African voices and personas are under-represented in these systems. As speech synthesis becomes increasingly democratized, it is desirable to increase the representation of African English accents. We present Afro-TTS, the first pan-African accented English speech synthesis system able to generate speech in 86 African accents, with 1000 personas representing the rich phonological diversity across the continent for downstream application in Education, Public Health, and Automated Content Creation. Speaker interpolation retains naturalness and accentedness, enabling the creation of new voices.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
