Speech is a rich biometric signal that contains information about the identity, gender and emotional state of the speaker. In this work, we explore its potential to generate face images of a speaker by conditioning a Generative Adversarial Network (GAN) with raw speech input. We propose a deep neural network that is trained from scratch in an end-to-end fashion, generating a face directly from the raw speech waveform without any additional identity information (e.g reference image or one-hot encoding). Our model is trained in a self-supervised fashion by exploiting the audio and visual signals naturally aligned in videos. With the purpose of training from video data, we present a novel dataset collected for this work, with high-quality videos of ten youtubers with notable expressiveness in both the speech and visual signals.

If you find this work useful, please consider citing:

Amanda Duarte, Francisco Roldan, Miquel Tubau, Janna Escur, Santiago Pascual, Amaia Salvador, Eva Mohedano, Kevin McGuinness, Jordi Torres, Xavier Giro-i-Nieto. “Wav2Pix: Speech-conditioned Face Generation using Generative Adversarial Networks”, ICASSP 2019.

@inproceedings{wav2pix2019icassp,
  title={Wav2Pix: Speech-conditioned Face Generation 
          using Generative Adversarial Networks},
  author={Amanda Duarte, Francisco Roldan, Miquel Tubau, Janna Escur, 
          Santiago Pascual, Amaia Salvador, Eva Mohedano, Kevin McGuinness, 
           Jordi Torres and Xavier Giro-i-Nieto},
  booktitle={2019 IEEE International Conference on Acoustics, Speech 
            and Signal Processing (ICASSP)},
  year={2019},
  organization={IEEE}
}

Download our paper in pdf here or find it on arXiv.