A Review on Emotion Recognition Algorithms using Speech Analysis

Teddy Surya Gunawan, Muhammad Fahreza Alghifari, Malik Arman Morshidi, Mira Kartiwi


In recent years, there is a growing interest in speech emotion recognition (SER) by analyzing input speech. SER can be considered as simply pattern recognition task which includes features extraction, classifier, and speech emotion database. The objective of this paper is to provide a comprehensive review on various literature available on SER. Several audio features are available, including linear predictive coding coefficients (LPCC), Mel-frequency cepstral coefficients (MFCC), and Teager energy based features. While for classifier, many algorithms are available including hidden Markov model (HMM), Gaussian mixture mdoel (GMM), vector quantization (VQ), artificial neural networks (ANN), and deep neural networks (DNN). In this paper, we also reviewed various speech emotion database. Finally, recent related works on SER using DNN will be discussed.


Speech Emotion Recogniton; MFCC; Deep Neural Networks; Emotion Database.

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Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
ISSN 2089-3272

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This work is licensed under a Creative Commons Attribution 4.0 International License.

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