# Wav2Pix: Speech-conditioned Face Generation using Generative Adversarial Networks Source: [Wav2Pix: Speech-conditioned Face Generation using Generative Adversarial Networks]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:28:43.855Z Authors: Amanda Duarte, Francisco Roldan, Miquel Tubau, Janna Escur, Santiago Pascual, Amaia Salvador, Eva Mohedano, Kevin McGuinness, Jordi Torres, Xavier Giro-i-Nieto Published: 2019-03-25T09:27:44.000Z ## Abstract 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 approach 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 youtubers with notable expressiveness in both the speech and visual signals. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Speech-to-Portrait on AVSpeech (test)](): Speech-to-Portrait; AVSpeech (test); L1 Error: 144.72. - [Speech-to-Portrait on VoxCeleb (test)](): Speech-to-Portrait; VoxCeleb (test); L1 Error: 137.58. ## Original sources and implementation [Original paper]() [PDF]()