Deep Learning is the recent machine learning technique that tries to model high level abstractions in databy using multiple processing layers with complex structures. It is also known as deep structured learning,hierarchical learning or deep machine learning. The term “deep learning" indicates the method used in trainingmulti-layered neural networks. Deep Learning technique has obtained remarkable success in the field of facerecognition with 97.5% accuracy. Facial Electromyogram (FEMG) signals are used to detect the different emotionsof humans. Some of the deep learning techniques discussed in this paper are Deep Boltzmann Machine (DBM), DeepBelief Networks (DBN), Convolutional Neural Networks (CNN) and Stacked Auto Encoders respectively. This paperfocuses on the review of some of the deep learning techniques used by various researchers which paved the way toimprove the classification accuracy of the FEMG signals as well as the speech signals

Deep Learning is the recent machine learning technique that tries to model high level abstractions in data by using multiple processing layers with complex structures. It is also known as deep structured learning, hierarchical learning or deep machine learning. The term “deep learning" indicates the method used in training multi-layered neural networks. Deep Learning technique has obtained remarkable success in the field of face recognition with 97.5% accuracy. Facial Electromyogram (FEMG) signals are used to detect the different emotions of humans. Some of the deep learning techniques discussed in this paper are Deep Boltzmann Machine (DBM), Deep Belief Networks (DBN), Convolutional Neural Networks (CNN) and Stacked Auto Encoders respectively. This paper focuses on the review of some of the deep learning techniques used by various researchers which paved the way to improve the classification accuracy of the FEMG signals as well as the speech signals.