注意力机制对生成对抗网络语音增强迁移学习模型的影响

基于深度学习的语音增强模型对训练集外语言语音和噪声进行降噪时,性能明显下降.为了解决这一问题,提出一种引入注意力机制的生成对抗网络(Generative Adversarial Network,GAN)语音增强迁移学习模型.在生成对抗语音增强模型的判别模型中引入注意力机制,以高资源场景下的大量语音数据训练得到的语音增强...

Influence of attention mechanism on generative adversarial network speech enhancement transfer learning model

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.