Speech emotion recognition: Emotional models, databases, features, preprocessing methods, supporting modalities, and classifiers

By Mehmet Berkehan Akçay, Kaya Oğuz. Speech Communication, 2020

615 citations recorded by the source corpus.

Abstract 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.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology, Signal Processing

View Paper: https://www.semanticscholar.org/paper/b71b889ab14820b81940ba1caeaf85c6ae9b7ffc

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Pradip Dhal
2. Chandrashekhar Azad

In Experimental and Cognitive Psychology

1. Pradip Dhal
2. Chandrashekhar Azad

In Signal Processing

1. Pradip Dhal
2. Chandrashekhar Azad

Citations in the corpus

• A comprehensive survey on feature selection in the various fields of machine learning (2021, methodology): “These features occurred in a connected speech when we bring speech or signal together [170].”