A systematic literature review of speech emotion recognition approaches

Abstract

Nowadays emotion recognition from speech (SER) is a demanding research area for researchers because of its wide real-life applications. There are many challenges for SER systems such as the availability of suitable emotional databases, identification of the relevant feature vector, and suitable classifiers. This paper critically analysed the literature on SER in terms of speech databases, speech features, traditional machine learning (ML) classifiers and DL approaches along with the areas for future directions. In recent years, there is a growing interest of researchers to use deep learning (DL) approaches for SER and get improvement in recognition rate. The focus of this review is on DL approaches for SER. A total of 152 papers have been reviewed from years 2000–2021. We have identified frequently used speech databases and related accuracies achieved using DL approaches. The motivations and limitations of DL approaches for SER are also summarized.