Speech processing, the field of analysing input speech signals and methods of processing them has emerged in the recent days. Additionally, the development of a speech processing system involves several components in the design phase with probabilistic approximations for enhanced audio sampling and de-noising. In this work, we focus into use of Gaussian random variables while modelling and filtering noise that gets added after being passed through an additive noise channel in a communication system, and the applications of Hidden Markov models. Moreover, we apply deep learning methods for emotion classification via a robust and accurate ensemble learning scheme that is applied to a joint deep network which incorporates audiovisual inputs and generates the emotion prediction effectively reaching satisfactory accuracy.