Representation and Metric Learning Advances for Face and Speaker Biometric Systems

Lecture given by Victoria Mingote, University of Zaragoza, Spain

Abstract: In recent years, as advanced as deep learning techniques are, they still have some problems when the task has limited data or a successful approach in one task is intended to be used for another task. Therefore, in this talk, I will present different alternative approaches to deal with these issues in biometric systems. First part of the talk is focused on different ways to improve the generation of signal representations for the text-dependent speaker verification task, since this task has a strong dependency of the phonetic content. While in the second part, I will explain several approaches using new training loss functions for deep neural networks that are based on the final verification metrics. These training loss functions can be applied to different verification tasks.

Biography: Victoria Mingote is a Postdoctoral researcher at ViVoLab research group. Her research interests expand through the areas of signal processing, machine learning, multimodal verification and identification (voice and face), and language identification.

Tags: biometric systems deep learning machine learning signal processing