Training Multi-Task Adversarial Network for Extracting Noise-Robust Speaker Embedding
This work addresses the problem of speaker recognition under noise for speech processing applications, representing an incremental improvement with a novel training strategy.
The paper tackles robust speaker recognition in noisy environments by proposing a multi-task adversarial training framework, which significantly outperforms non-adversarial methods in noisy conditions and also improves performance in clean settings.
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.