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Generating a voice from a face image is crucial for developing virtual humanscapable of interacting using their unique voices, without relying onpre-recorded human speech. In this paper, we propose Face-StyleSpeech, azero-shot Text-To-Speech (TTS) synthesis model that generates natural speechconditioned on a face image rather than reference speech. We hypothesize thatlearning both speaker identity and prosody from a face image poses asignificant challenge. To address the issue, our TTS model incorporates both aface encoder and a prosody encoder. The prosody encoder is specificallydesigned to model prosodic features that are not captured only with a faceimage, allowing the face encoder to focus solely on capturing the speakeridentity from the face image. Experimental results demonstrate thatFace-StyleSpeech effectively generates more natural speech from a face imagethan baselines, even for the face images the model has not trained. Samples areat our demo page https://face-stylespeech.github.io.

Face-StyleSpeech: Improved Face-to-Voice latent mapping for Natural Zero-shot Speech Synthesis from a Face Image