Improved Wasserstein conditional generative adversarial network speech enhancement

The speech enhancement based on the generative adversarial network has achieved excellent results with large quantities of data, but performance in the low-data regime and tasks like unseen data learning still lag behind. In this work, we model Wasserstein Conditional Generative Adversarial Network-Gradient Penalty speech enhancement system and introduce the elastic network into the objective function to simplify and improve the performance of the model in low-resource data environment. We argue that the regularization is significant in learning with small amounts of data and the available information of the input data is key in speech enhancement performance and generalization ability of the model, which means that network parameters and network structure can be set up and designed according to the characteristics of actual input data. Experiments on the noisy speech corpus show that the improved algorithm outperforms previous generative adversarial network speech enhancement approach.