The advancements in neural networks and the on-demand need for accurate and near real-time Speech Emotion Recognition (SER) in human-computer interactions make it mandatory to compare available methods and databases in SER to achieve feasible solutions and a firmer understanding of this open-ended problem. The current study reviews deep learning approaches for SER with available datasets, followed by conventional machine learning techniques for speech emotion recognition. Ultimately, we present a multi-aspect comparison between practical neural network approaches in speech emotion recognition. The goal of this study is to provide a survey of the field of discrete speech emotion recognition.
Keywords: CNN; GAN; LSTM; attention mechanism; autoencoders; deep learning; emotional speech database; machine learning; speech emotion recognition.