Method for reducing the feature space dimension in speech emotion recognition using convolutional neural networks

We consider the architectures of convolutional neural networks used to assess the emotional state of a person by their speech. The problem of increasing the efficiency of emotion recognition by reducing the computational complexity of this process is solved. To this end, we propose a method transforming the input data into a form suitable for machine learning algorithms.