Improving the Performance of Deep Learning Based Speech Enhancement System Using Fuzzy Restricted Boltzmann Machine

Supervised speech enhancement based on machine learning is a new paradigm for segregating clean speech from background noise. The current work represents a supervised speech enhancement system based on a robust deep learning method where the pre-training phase of deep belief network (DBN) has been conducted by employing fuzzy restricted Boltzmann machines (FRBM) instead of regular RBM. It has been observed that the performance of FRBM model is superior to that of RBM model particularly when the training data is noisy. Our experimental results on various noise scenarios have shown that the proposed approach outperforms the conventional DNN-based speech enhancement methods which use regular RBM for unsupervised pre-training.