Improving speech system performance in noisy environments remains a
challenging task, and speech enhancement (SE) is one of the effective
techniques to solve the problem. Motivated by the promising results of
generative adversarial networks (GANs) in a variety of image processing tasks,
we explore the potential of conditional GANs (cGANs) for SE, and in particular,
we make use of the image processing framework proposed by Isola et al. [1] to
learn a mapping from the spectrogram of noisy speech to an enhanced
counterpart. The SE cGAN consists of two networks, trained in an adversarial
manner: a generator that tries to enhance the input noisy spectrogram, and a
discriminator that tries to distinguish between enhanced spectrograms provided
by the generator and clean ones from the database using the noisy spectrogram
as a condition. We evaluate the performance of the cGAN method in terms of
perceptual evaluation of speech quality (PESQ), short-time objective
intelligibility (STOI), and equal error rate (EER) of speaker verification (an
example application). Experimental results show that the cGAN method overall
outperforms the classical short-time spectral amplitude minimum mean square
error (STSA-MMSE) SE algorithm, and is comparable to a deep neural
network-based SE approach (DNN-SE).

利用条件生成对抗网络（cGANs）从噪声语音的谱图到增强副本，将其应用于语音增强（SE）中，并以感知语音质量（PESQ）、短时客观可懂度（STOI）和说话人验证等方面进行评估。实验结果显示，cGAN 方法整体上优于传统的 STSA-MMSE SE 算法，并且与基于深度神经网络的 SE 方法 (DNN-SE) 相当。