•Current methodologies in Speech Emotion Recognition (SER) technologies are reviewed.•The review includes all related areas of SER; emotional models, databases, features, preprocessing methods, supporting modalities, and classifiers.•The review covers the recent developments in classifiers, such as the usage of convolutional and recurrent neural networks.•A list of existing challenges is also discussed. Speech is the most natural way of expressing ourselves as humans. It is only natural then to extend this communication medium to computer applications. We define speech emotion recognition (SER) systems as a collection of methodologies that process and classify speech signals to detect the embedded emotions. SER is not a new field, it has been around for over two decades, and has regained attention thanks to the recent advancements. These novel studies make use of the advances in all fields of computing and technology, making it necessary to have an update on the current methodologies and techniques that make SER possible. We have identified and discussed distinct areas of SER, provided a detailed survey of current literature of each, and also listed the current challenges.