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In this paper, we propose a multi-speaker face-to-speech waveform generationmodel that also works for unseen speaker conditions. Using a generativeadversarial network (GAN) with linguistic and speaker characteristic featuresas auxiliary conditions, our method directly converts face images into speechwaveforms under an end-to-end training framework. The linguistic features areextracted from lip movements using a lip-reading model, and the speakercharacteristic features are predicted from face images using cross-modallearning with a pre-trained acoustic model. Since these two features areuncorrelated and controlled independently, we can flexibly synthesize speechwaveforms whose speaker characteristics vary depending on the input faceimages. We show the superiority of our proposed model over conventional methodsin terms of objective and subjective evaluation results. Specifically, weevaluate the performances of linguistic features by measuring their accuracy onan automatic speech recognition task. In addition, we estimate speaker andgender similarity for multi-speaker and unseen conditions, respectively. Wealso evaluate the aturalness of the synthesized speech waveforms using a meanopinion score (MOS) test and non-intrusive objective speech quality assessment(NISQA).The demo samples of the proposed and other models are available athttps://sam-0927.github.io/

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