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Hear Your Face: Face-based voice conversion with F0 estimation
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This paper delves into the emerging field of face-based voice conversion, leveraging the unique relationship between an individual's facial features and their vocal characteristics. We present a novel face-based voice conversion framework that particularly utilizes the average fundamental frequency of the target speaker, derived solely from their facial images. Through extensive analysis, our framework demonstrates superior speech generation quality and the ability to align facial features with voice characteristics, including tracking of the target speaker's fundamental frequency.
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Cited by 2 Pith papers
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Emotional Face-to-Speech
A discrete diffusion transformer with curriculum learning generates emotional speech from identity and emotion cues extracted from a face image.
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MultiActor-Audiobook: Zero-Shot Audiobook Generation with Faces and Voices of Multiple Speakers
A zero-shot pipeline that creates character voices from AI-generated faces and LLM-written prosody instructions can produce expressive audiobooks without extra training or manual annotation, though human quality score...
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