Speech enhancement based on improved deep neural networks with MMSE pretreatment features
Wei Han, Congming Wu, Xiongwei Zhang, Meng Sun, Gang Min · 2016
Speech enhancement plays an important role in robust speech processing. Deep learning has become a new trend towards solving speech enhancement problems. The input feature is a key aspect of deep learning, which effect the enhancement performance. In this paper, we explore a new feature which extract through the minimum mean square error (MMSE) estimator pretreatment. Incorporating the MMSE pretreatment features, we proposed a novel deep neural network (DNN) for speech enhancement task. Evaluation experiments on TIMIT database with 20 noise types at different signal-to-noise ratio (SNR) situations demonstrate the effectiveness of the proposed approach compared with the reference DNN-based enhancement approaches, no matter whether the noise matched the training set or not.