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We propose PSMGAN, an efficient phase sensitive masking-based single-channel speech enhancement technique using a conditional generative adversarial network (cGAN). The time–frequency (T-F) masking-based speech enhancement approaches through deep neural networks (DNNs) have shown large speech intelligibility improvements. However, these approaches fail to achieve better enhancement results at low signal-to-noise ratio (SNR) conditions since they ignore the phase information during reconstruction. Alternatively, GANs have been introduced effectively for speech enhancement and achieved improved performance due to the adversarial training. Motivated by the recent success of GAN, we introduce the phase sensitive masking (PSM) in a cGAN framework for speech enhancement task. The reason for choosing a conditional generative model is that the data generation process can be …