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.

Afro-TTS 是第一个能够在 86 种非洲口音中生成语音的泛非洲口音英语语音合成系统，包含了代表该大陆丰富的音韵多样性的 1000 个人物形象，可用于教育、公共卫生和自动内容创作。说话者插值保持了自然和口音特点，使得能够创造新的声音。

1000 名非洲人的声音：推进包容性多说话者多口音语音合成

1000 African Voices: Advancing inclusive multi-speaker multi-accent  speech synthesis

We introduce Text2Immersion, an elegant method for producing high-quality 3D
immersive scenes from text prompts. Our proposed pipeline initiates by
progressively generating a Gaussian cloud using pre-trained 2D diffusion and
depth estimation models. This is followed by a refining stage on the Gaussian
cloud, interpolating and refining it to enhance the details of the generated
scene. Distinct from prevalent methods that focus on single object or indoor
scenes, or employ zoom-out trajectories, our approach generates diverse scenes
with various objects, even extending to the creation of imaginary scenes.
Consequently, Text2Immersion can have wide-ranging implications for various
applications such as virtual reality, game development, and automated content
creation. Extensive evaluations demonstrate that our system surpasses other
methods in rendering quality and diversity, further progressing towards
text-driven 3D scene generation. We will make the source code publicly
accessible at the project page.

Text2Immersion 是一种从文本提示生成高质量 3D 沉浸式场景的优雅方法，可以生成多样化的场景，进一步推动了基于文本的 3D 场景生成。