## 1 Introduction  
Single-channel speech enhancement has been studied for decades, while it is still a challenging problem in numerous application systems such as automatic speech recognition (ASR), hearing aids and hands-free mobile communication. Traditional speech enhancement algorithms are usually based on a statistical method, and consist of noise spectrum estimation and speech signal estimation [1; 2; 3]. The performance of these methods relies heavily on accurate noise estimation. However, due to the statistical assumption of the speech and noise, most of the traditional methods often fail to track non-stationary noise and lead to a high level of musical noise artifacts.  
In recent years, numerous data-driven methods have been proposed to circumvent the assumption of specific distortion for the speech and noise processes [4; 5]. One of the notable algorithms is the regression approach to speech enhancement based on deep neural networks (DNN), which is inspired by the successful introduction of DNN to acoustic modeling in ASR system [6]. Another kind of popular method is using the DNN to estimate an ideal binary mask (IBM) or a smoothed ideal ratio mask (IRM) in the frequency domain, which is derived from computational auditory scene analysis for monaural speech separation [7; 8]. Nevertheless, all these methods only enhance the speech magnitude spectrum, leaving the phase spectrum unprocessed.  
More recently, an end-to-end speech enhancement method is proposed using generative adversarial networks (GAN) [9], namely, speech enhancement GAN (SEGAN). The SEGAN, which is an end-to-end system, operates speech signal on the raw waveform, other than on the spectral domain or on some higher-level domain. The experimental results show that the GAN-based method achieves better performance than traditional methods both in objective and subjective evaluations. Yet, all the DNN-based approaches are data-hungry: The more data is available, the better performance they gain. In the absence of more data, researchers usually train many different models on the same data and then average their predictions. Whereas, using a whole ensemble of models is too computationally expensive. To overcome this limitation, a compression method is proposed to distill the knowledge from an ensemble of neural network models [10]. This encourages us to distill knowledge from the traditional minimum mean squared error (MMSE) method, e.g., the optimally-modified log-spectral amplitude (OMLSA) speech estimator and minima controlled recursive averaging (MCRA) noise estimation approach for robust speech enhancement [2], denoted as OMLSA method.  
In this paper, we propose a novel DNN-based speech enhancement method using adversarial training. The main challenge of applying DNN to speech enhancement is how to get more training pairs, especially for real data cases, where the ground-truth clean speech is not available. We use the traditional statistical-based speech enhancement method to estimate the speech signal in the real case, so that we can obtain more training pairs for the DNN. That is, we train a DNN-based model by distilling knowledge from the traditional statistical-based method. Moreover, in the view of machine learning methodology, while most of the existing DNN-based methods are trained using supervised approach [6; 7; 8; 9], the proposed method is trained in a semi-supervised manner.  
The rest of the paper is organized as follows. In Section 2, we briefly introduce the related work. Section 3 presents the proposed new speech enhancement method. A set of experimental results are provided in Section 4. Finally, we summarize our work in Section 5.