Late Reverberation Suppression Using Recurrent Neural Networks with Long Short-Term Memory

Human speech is usually distorted by room reverberation. These corruptions degrade speech quality and intelligibility, especially under a long reverberation time, and they also pose a serious problem for many speech-related applications such as automatic speech recognition. In this paper, we propose a supervised speech dereverberation algorithm that models late reverberation using a recurrent neural network (RNN) with long short-term memory (LSTM). By taking advantage of LSTM's ability to capture a long history, late reverberation can be effectively removed by the proposed approach. Systematic evaluations indicate that our approach improves the quality of reverberant speech in a wide range of reverberant conditions. Moreover, the proposed system is a causal system, which can be applied in real-time applications.