Face Image Synthesis From Speech Using Conditional Generative A Adversarial Network

A Human Brain may translate a person's voice to its corresponding face image even if never seen before. Training a deep learning network to do the same can be used in detecting human faces based on their voice, which may be used in finding a criminal that we only have a voice recording for. The goal in this paper is to build a Conditional Generative Adversarial Network that produces face images from human speeches which can then be recognized by a face recognition model to identify the owner of the speech. The model was trained and the face recognition model gave an accuracy of 80.08% in training and 56.2% in testing. Compared to the basic GAN model, this model has improved the results by about 30%.