Facetron: A Multi-speaker Face-to-Speech Model based on Cross-Modal Latent Representations

International Conference
2023-08-11 11:28
Authors : Seyun Um, Jihyun Kim, Jihyun Lee, Hong-Goo Kang

Year : 2023

Publisher / Conference : EUSIPCO

Research area : Speech Signal Processing, Audio-Visual

Presentation : Poster

In this paper, we propose a multi-speaker face-to-speech waveform generation model that also works for unseen speaker conditions. Using a generative adversarial network (GAN) with linguistic and speaker characteristic features as auxiliary conditions, our method directly converts face images into speech waveforms under an end-to-end training framework. The linguistic features are extracted from lip movements using a lip-reading model, and the speaker characteristic features are predicted from face images using a face encoder trained through cross-modal learning with a pre-trained acoustic model. Since these two features are uncorrelated and controlled independently, we can flexibly synthesize speech waveforms whose speaker characteristics vary depending on the input face images. We show the superiority of our proposed model over conventional methods in terms of objective and subjective evaluation results.
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