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Generating speech 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 entire prosodic features from a face image poses a significantchallenge. To address this, our TTS model incorporates both face and prosodyencoders. The prosody encoder is specifically designed to model speech stylecharacteristics that are not fully captured by the face image, allowing theface encoder to focus on extracting speaker-specific features such as timbre.Experimental results demonstrate that Face-StyleSpeech effectively generatesmore natural speech from a face image than baselines, even for unseen faces.Samples are available on our demo page.

Face-StyleSpeech: Enhancing Zero-shot Speech Synthesis from Face Images with Improved Face-to-Speech Mapping