Speech Enhancement Using Generative Adversarial Network by Distilling Knowledge from Statistical Method

• Main Authors: Jianfeng Wu, Yongzhu Hua, Shengying Yang, Hongshuai Qin, Huibin Qin
• Format: Article
• Language: English
• Published: MDPI AG 2019-08-01
• Series: Applied Sciences
• Subjects: speech enhancement; deep neural network; generative adversarial network; distill knowledge
• Online Access: https://www.mdpi.com/2076-3417/9/16/3396

This paper presents a new deep neural network (DNN)-based speech enhancement algorithm by integrating the distilled knowledge from the traditional statistical-based method. Unlike the other DNN-based methods, which usually train many different models on the same data and then average their predictions, or use a large number of noise types to enlarge the simulated noisy speech, the proposed method does not train a whole ensemble of models and does not require a mass of simulated noisy speech. It first trains a discriminator network and a generator network simultaneously using the adversarial learning method. Then, the discriminator network and generator network are re-trained by distilling knowledge from the statistical method, which is inspired by the knowledge distillation in a neural network. Finally, the generator network is fine-tuned using real noisy speech. Experiments on CHiME4 data sets demonstrate that the proposed method achieves a more robust performance than the compared DNN-based method in terms of perceptual speech quality.