The study of emotions is very tough job which sounds in the form to recognize the facial expressions of human face. This problem is quite challenging because of various face architecture of the facial parts of human. In fact it is variant with locality. It becomes hot topic of research for hearing impaired people. Facial expression is considered as a part of sign language recognition (SLR).The pre-processing involves face detection using the algorithm proposed by Viola and Jones using Haar-cascades for facial part detection. In this work, we purpose a fusion of texture local binary pattern, landmark based active shape model (ASM) of geometric features. The features are trained and tested by multi-class support vector machine (SVM).

Audio is one of the most used ways of human communication, but at the same time it can be easily misused to trick people. With the revolution of AI, the related technologies are now accessible to almost everyone, thus making it simple for the criminals to commit crimes and forgeries. In this work, we introduce a neural network method to develop a classifier that will blindly classify an input audio as real or mimicked; the word ‘blindly’ refers to the ability to detect mimicked audio without references or real sources. We propose a deep neural network following a sequential model that comprises three hidden layers, with alternating dense and drop out layers. The proposed model was trained on a set of 26 important features extracted from a large dataset of audios to get a classifier that was tested on the same set of features from different audios. The data was extracted from two raw datasets, especially composed for this work; an all English dataset and a mixed dataset (Arabic plus English). For the purpose of comparison, the audios were also classified through human inspection with the subjects being the native speakers. The ensued results were interesting and exhibited formidable accuracy, as we were able to get at least $$94\%$$94 % correct classification of the test cases, as against the $$85\%$$85 % accuracy in the case of human observers.