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This work seeks the possibility of generating the human face from voicesolely based on the audio-visual data without any human-labeled annotations. Tothis end, we propose a multi-modal learning framework that links the inferencestage and generation stage. First, the inference networks are trained to matchthe speaker identity between the two different modalities. Then the trainedinference networks cooperate with the generation network by giving conditionalinformation about the voice. The proposed method exploits the recentdevelopment of GANs techniques and generates the human face directly from thespeech waveform making our system fully end-to-end. We analyze the extent towhich the network can naturally disentangle two latent factors that contributeto the generation of a face image - one that comes directly from a speechsignal and the other that is not related to it - and explore whether thenetwork can learn to generate natural human face image distribution by modelingthese factors. Experimental results show that the proposed network can not onlymatch the relationship between the human face and speech, but can also generatethe high-quality human face sample conditioned on its speech. Finally, thecorrelation between the generated face and the corresponding speech isquantitatively measured to analyze the relationship between the two modalities.

From Inference to Generation: End-to-end Fully Self-supervised Generation of Human Face from Speech