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

• URL: http://arxiv.org/abs/2311.05844v1
• Date: Mon, 25 Sep 2023 13:46:00 GMT
• Title: Face-StyleSpeech: Improved Face-to-Voice latent mapping for Natural Zero-shot Speech Synthesis from a Face Image
• Authors: Minki Kang, Wooseok Han, Eunho Yang
• Abstract summary: We propose Face-StyleSpeech, a zero-shot Text-To-Speech model that generates natural speech conditioned on a face image. Experimental results demonstrate that Face-StyleSpeech effectively generates more natural speech from a face image than baselines.
• Score: 42.23406025068276
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generating a voice from a face image is crucial for developing virtual humans capable of interacting using their unique voices, without relying on pre-recorded human speech. In this paper, we propose Face-StyleSpeech, a zero-shot Text-To-Speech (TTS) synthesis model that generates natural speech conditioned on a face image rather than reference speech. We hypothesize that learning both speaker identity and prosody from a face image poses a significant challenge. To address the issue, our TTS model incorporates both a face encoder and a prosody encoder. The prosody encoder is specifically designed to model prosodic features that are not captured only with a face image, allowing the face encoder to focus solely on capturing the speaker identity from the face image. Experimental results demonstrate that Face-StyleSpeech effectively generates more natural speech from a face image than baselines, even for the face images the model has not trained. Samples are at our demo page https://face-stylespeech.github.io.

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