Speech Enhancement Network with Unsupervised Attention using Invariant Information Clustering

In this paper, we propose a new framework for speech enhancement using supervised attention trained by Invariant Information Clustering (IIC). For suppressing an overfitting in the speech enhancement network, the multitask learning with the speaker-invariant information is adopted at the latent representation layer. Several simulations reveal the effectiveness of this method through the speech enhancement experiments.