Title: Training Multi-Task Adversarial Network For Extracting Noise-Robust Speaker Embedding,
Abstract: Under noisy environments, to achieve the robust performance of speaker
recognition is still a challenging task. Motivated by the promising performance
of multi-task training in a variety of image processing tasks, we explore the
potential of multi-task adversarial training for learning a noise-robust
speaker embedding. In this paper we present a novel framework which consists of
three components: an encoder that extracts noise-robust speaker embedding; a
classifier that classifies the speakers; a discriminator that discriminates the
noise type of the speaker embedding. Besides, we propose a training strategy
using the training accuracy as an indicator to stabilize the multi-class
adversarial optimization process. We conduct our experiments on the English and
Mandarin corpus and the experimental results demonstrate that our proposed
multi-task adversarial training method could greatly outperform the other
methods without adversarial training in noisy environments. Furthermore,
experiments indicate that our method is also able to improve the speaker
verification performance the clean condition.