Subject: [AUDITORY] Reconstructing faces from voices From: bhiksha raj <bhiksha@xxxxxxxx> Date: Tue, 28 May 2019 07:27:37 -0400 List-Archive:<http://lists.mcgill.ca/scripts/wa.exe?LIST=AUDITORY>Dear All Thought you might be interested in this work by Yandong Wen and Rita Singh at CMU. (I'm a co-author by virtue of being in an advisory role) https://arxiv.org/abs/1905.10604 Abstract: Voice profiling aims at inferring various human parameters from their speech, e.g. gender, age, etc. In this paper, we address the challenge posed by a subtask of voice profiling - reconstructing someone's face from their voice. The task is designed to answer the question: given an audio clip spoken by an unseen person, can we picture a face that has as many common elements, or associations as possible with the speaker, in terms of identity? To address this problem, we propose a simple but effective computational framework based on generative adversarial networks (GANs). The network learns to generate faces from voices by matching the identities of generated faces to those of the speakers, on a training set. We evaluate the performance of the network by leveraging a closely related task - cross-modal matching. The results show that our model is able to generate faces that match several biometric characteristics of the speaker, and results in matching accuracies that are much better than chance. best Bhiksha -- Bhiksha Raj Carnegie Mellon University Pittsburgh, PA, USA Tel: 412 268 9826