Training Multi-task Adversarial Network for Extracting Noise-robust Speaker Embedding

• Published in: ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 6196 - 6200
• Main Authors: Zhou, Jianfeng, Jiang, Tao, Li, Lin, Hong, Qingyang, Wang, Zhe, Xia, Bingyin
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2019
• Subjects: adversarial training; multi-task; speaker embedding; speaker verification
• ISSN: 2379-190X
• DOI: 10.1109/ICASSP.2019.8683828

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 multitask adversarial training for learning a noise-robust speaker embedding. In this paper, we present a novel framework that consists of three components: an encoder that extracts the noise-robust speaker embeddings; a classifier that classifies the speakers; a discriminator that discriminates the noise type of the speaker embeddings. Additionally , 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 corpuses 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, the experiments indicate that our method is also able to improve the speaker verification performance under the clean condition.