A Review on Automatic Speech Emotion Recognition with an Experiment Using Multilayer Perceptron Classifier

Abdullah Al Mamun Sardar; Md Sanzidul Islam; Touhid Bhuiyan

doi:10.1007/978-981-15-7394-1_36

A Review on Automatic Speech Emotion Recognition with an Experiment Using Multilayer Perceptron Classifier

Abdullah Al Mamun Sardar, Md Sanzidul Islam, Touhid Bhuiyan

Research output: Chapter in Book/Report/Conference proceeding › Chapter

2 Citations (Scopus)

Abstract

Human–machine interaction is becoming popular day by day; to interact with machine, speech emotion recognition is as important as human to human interaction. In this research, we demonstrate a speech emotion recognition system which takes speech as input and classify emotions that the speech contains. We choose multilayer perceptron (MLP) classifier to do this task. Features that we have extracted from speech are mel-frequency cepstral coefficients (MFCC), chroma and mel-spectrogram frequency. RADVES dataset has been used and we have got 73% accuracy.

Original language	English
Title of host publication	Advances in Intelligent Systems and Computing
Pages	381-388
Number of pages	8
ISBN (Electronic)	9789811573934
DOIs	https://doi.org/10.1007/978-981-15-7394-1_36
Publication status	Published - 28 Nov 2020
Externally published	Yes
Event	Advances in Intelligent Systems and Computing - Duration: 1 Jan 2021 → …

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	1248

Conference

Conference	Advances in Intelligent Systems and Computing
Period	1/01/21 → …

Keywords

Chroma
MFCC
MLP classifier
Mel-spectrogram frequency
Speech emotion recognition

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10.1007/978-981-15-7394-1_36

Cite this

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title = "A Review on Automatic Speech Emotion Recognition with an Experiment Using Multilayer Perceptron Classifier",

abstract = "Human–machine interaction is becoming popular day by day; to interact with machine, speech emotion recognition is as important as human to human interaction. In this research, we demonstrate a speech emotion recognition system which takes speech as input and classify emotions that the speech contains. We choose multilayer perceptron (MLP) classifier to do this task. Features that we have extracted from speech are mel-frequency cepstral coefficients (MFCC), chroma and mel-spectrogram frequency. RADVES dataset has been used and we have got 73% accuracy.",

keywords = "Chroma, MFCC, MLP classifier, Mel-spectrogram frequency, Speech emotion recognition",

author = "Sardar, {Abdullah Al Mamun} and {Sanzidul Islam}, Md and Touhid Bhuiyan",

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month = nov,

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doi = "10.1007/978-981-15-7394-1_36",

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series = "Advances in Intelligent Systems and Computing",

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note = "Advances in Intelligent Systems and Computing ; Conference date: 01-01-2021",

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Sardar, AAM, Sanzidul Islam, M & Bhuiyan, T 2020, A Review on Automatic Speech Emotion Recognition with an Experiment Using Multilayer Perceptron Classifier. in Advances in Intelligent Systems and Computing. Advances in Intelligent Systems and Computing, vol. 1248, pp. 381-388, Advances in Intelligent Systems and Computing, 1/01/21. https://doi.org/10.1007/978-981-15-7394-1_36

A Review on Automatic Speech Emotion Recognition with an Experiment Using Multilayer Perceptron Classifier. / Sardar, Abdullah Al Mamun; Sanzidul Islam, Md; Bhuiyan, Touhid.
Advances in Intelligent Systems and Computing. 2020. p. 381-388 (Advances in Intelligent Systems and Computing; Vol. 1248).

Research output: Chapter in Book/Report/Conference proceeding › Chapter

TY - CHAP

T1 - A Review on Automatic Speech Emotion Recognition with an Experiment Using Multilayer Perceptron Classifier

AU - Sardar, Abdullah Al Mamun

AU - Sanzidul Islam, Md

AU - Bhuiyan, Touhid

PY - 2020/11/28

Y1 - 2020/11/28

N2 - Human–machine interaction is becoming popular day by day; to interact with machine, speech emotion recognition is as important as human to human interaction. In this research, we demonstrate a speech emotion recognition system which takes speech as input and classify emotions that the speech contains. We choose multilayer perceptron (MLP) classifier to do this task. Features that we have extracted from speech are mel-frequency cepstral coefficients (MFCC), chroma and mel-spectrogram frequency. RADVES dataset has been used and we have got 73% accuracy.

AB - Human–machine interaction is becoming popular day by day; to interact with machine, speech emotion recognition is as important as human to human interaction. In this research, we demonstrate a speech emotion recognition system which takes speech as input and classify emotions that the speech contains. We choose multilayer perceptron (MLP) classifier to do this task. Features that we have extracted from speech are mel-frequency cepstral coefficients (MFCC), chroma and mel-spectrogram frequency. RADVES dataset has been used and we have got 73% accuracy.

KW - Chroma

KW - MFCC

KW - MLP classifier

KW - Mel-spectrogram frequency

KW - Speech emotion recognition

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DO - 10.1007/978-981-15-7394-1_36

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T3 - Advances in Intelligent Systems and Computing

SP - 381

EP - 388

BT - Advances in Intelligent Systems and Computing

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Y2 - 1 January 2021

ER -

A Review on Automatic Speech Emotion Recognition with an Experiment Using Multilayer Perceptron Classifier

Abstract

Publication series

Conference

Keywords

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