Paper Info
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 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.
Year
Venue
Keywords
2018
2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
multi-task, speaker embedding, adversarial training, speaker verification
Field
DocType
Volume
Discriminator,Embedding,Pattern recognition,Computer science,Image processing,Speaker recognition,Artificial intelligence,Encoder,Classifier (linguistics),Mandarin Chinese,Adversarial system
Journal
abs/1811.09355
ISSN
Citations 
PageRank 
1520-6149
1
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Jianfeng Zhou151.11
Tao Jiang250.77
Lin Li3124.60
Q. Y. Hong45015.79
Zhe Wang511.02
Bingyin Xia610.34