Abstract: The deep learning based speech enhancement model encounters the problem of enhancement performance degradation when de-noising the unseen languages and noise in training sets. In order to solve this problem, a generative adversarial network (GAN) speech enhancement transfer learning model with attention mechanism (called ATGAN speech enhancement model) is proposed in this paper. The attention mechanism is introduced into the discriminator of GAN speech enhancement model. Based on the well-trained model obtained with high-resource materials and combining a small amount of speech training data in low-resource condition, the weight transfer of the basic enhancement model trained with low-resource data is carried out to improve the enhancement effect in low-resource condition. Experiments show that the use of ATGAN speech enhancement model can effectively enhance the denoising effect of low-resource noisy speech.