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Deep learning for emotional speech recognition

Emotion speech recognition is a developing field in machine learning. The main purpose of this field is to produce a convenient system that is able to effortlessly communicate and interact with humans. Speech signals are loaded with information which is divided into two main categories, linguistic a...

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Main Authors: Alhamada, M. I., Khalifa, Othman Omran, Hassan Abdalla Hashim, Aisha
Format: Conference or Workshop Item
Language:English
English
Published: AIP Publishing 2020
Subjects:
T Technology (General)
T10.5 Communication of technical information
Online Access:http://irep.iium.edu.my/82389/8/Certificate%20ICEDSA%202020%20%20%2328%20Deep%20Learning%20for%20Emotional%20Speech%20Recognition.pdf
http://irep.iium.edu.my/82389/18/82389%20Deep%20learning%20for%20emotional%20speech%20recognition.pdf
http://irep.iium.edu.my/82389/
https://aip.scitation.org/doi/pdf/10.1063/5.0032381
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spelling my.iium.irep.823892020-12-30T04:15:20Z http://irep.iium.edu.my/82389/ Deep learning for emotional speech recognition Alhamada, M. I. Khalifa, Othman Omran Hassan Abdalla Hashim, Aisha T Technology (General) T10.5 Communication of technical information Emotion speech recognition is a developing field in machine learning. The main purpose of this field is to produce a convenient system that is able to effortlessly communicate and interact with humans. Speech signals are loaded with information which is divided into two main categories, linguistic and paralinguistic; emotions belong to the latter tree. Developing systems that can understand paralinguistic information is paramount for better human-machine interactions. The complete reliability of the current speech emotion recognition systems is far from being achieved. To wit, the objective of this project is to review different methods used in speech emotion recognition SER. Different extracted features like MFCC as well as feature classifications methods like HMM, GMM, LTSTM and ANN are also researched. This research will also investigate different speech emotion databases that are commonly used. Finally, this paper implements an architecture of CNN that is used for speech emotion recognition. The proposed CNN model achieved 93.96% accuracy rate in detecting 5 emotions. AIP Publishing 2020-12-15 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/82389/8/Certificate%20ICEDSA%202020%20%20%2328%20Deep%20Learning%20for%20Emotional%20Speech%20Recognition.pdf application/pdf en http://irep.iium.edu.my/82389/18/82389%20Deep%20learning%20for%20emotional%20speech%20recognition.pdf Alhamada, M. I. and Khalifa, Othman Omran and Hassan Abdalla Hashim, Aisha (2020) Deep learning for emotional speech recognition. In: 7th International Conference on Electronic Devices, Systems and Applications (ICEDSA2020), 28th - 29th March 2020, Shah Alam, Malaysia. https://aip.scitation.org/doi/pdf/10.1063/5.0032381 10.1063/5.0032381
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
English
topic T Technology (General)
T10.5 Communication of technical information
spellingShingle T Technology (General)
T10.5 Communication of technical information
Alhamada, M. I.
Khalifa, Othman Omran
Hassan Abdalla Hashim, Aisha
Deep learning for emotional speech recognition
description Emotion speech recognition is a developing field in machine learning. The main purpose of this field is to produce a convenient system that is able to effortlessly communicate and interact with humans. Speech signals are loaded with information which is divided into two main categories, linguistic and paralinguistic; emotions belong to the latter tree. Developing systems that can understand paralinguistic information is paramount for better human-machine interactions. The complete reliability of the current speech emotion recognition systems is far from being achieved. To wit, the objective of this project is to review different methods used in speech emotion recognition SER. Different extracted features like MFCC as well as feature classifications methods like HMM, GMM, LTSTM and ANN are also researched. This research will also investigate different speech emotion databases that are commonly used. Finally, this paper implements an architecture of CNN that is used for speech emotion recognition. The proposed CNN model achieved 93.96% accuracy rate in detecting 5 emotions.
format Conference or Workshop Item
author Alhamada, M. I.
Khalifa, Othman Omran
Hassan Abdalla Hashim, Aisha
author_facet Alhamada, M. I.
Khalifa, Othman Omran
Hassan Abdalla Hashim, Aisha
author_sort Alhamada, M. I.
title Deep learning for emotional speech recognition
title_short Deep learning for emotional speech recognition
title_full Deep learning for emotional speech recognition
title_fullStr Deep learning for emotional speech recognition
title_full_unstemmed Deep learning for emotional speech recognition
title_sort deep learning for emotional speech recognition
publisher AIP Publishing
publishDate 2020
url http://irep.iium.edu.my/82389/8/Certificate%20ICEDSA%202020%20%20%2328%20Deep%20Learning%20for%20Emotional%20Speech%20Recognition.pdf
http://irep.iium.edu.my/82389/18/82389%20Deep%20learning%20for%20emotional%20speech%20recognition.pdf
http://irep.iium.edu.my/82389/
https://aip.scitation.org/doi/pdf/10.1063/5.0032381
_version_ 1688547728068444160
score 13.211869

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