Cargando…
Human-Computer Interaction for Recognizing Speech Emotions Using Multilayer Perceptron Classifier
Human-computer interaction (HCI) has seen a paradigm shift from textual or display-based control toward more intuitive control modalities such as voice, gesture, and mimicry. Particularly, speech has a great deal of information, conveying information about the speaker's inner condition and his/...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8979705/ https://www.ncbi.nlm.nih.gov/pubmed/35388315 http://dx.doi.org/10.1155/2022/6005446 |
_version_ | 1784681232931487744 |
---|---|
author | Alnuaim, Abeer Ali Zakariah, Mohammed Shukla, Prashant Kumar Alhadlaq, Aseel Hatamleh, Wesam Atef Tarazi, Hussam Sureshbabu, R. Ratna, Rajnish |
author_facet | Alnuaim, Abeer Ali Zakariah, Mohammed Shukla, Prashant Kumar Alhadlaq, Aseel Hatamleh, Wesam Atef Tarazi, Hussam Sureshbabu, R. Ratna, Rajnish |
author_sort | Alnuaim, Abeer Ali |
collection | PubMed |
description | Human-computer interaction (HCI) has seen a paradigm shift from textual or display-based control toward more intuitive control modalities such as voice, gesture, and mimicry. Particularly, speech has a great deal of information, conveying information about the speaker's inner condition and his/her aim and desire. While word analysis enables the speaker's request to be understood, other speech features disclose the speaker's mood, purpose, and motive. As a result, emotion recognition from speech has become critical in current human-computer interaction systems. Moreover, the findings of the several professions involved in emotion recognition are difficult to combine. Many sound analysis methods have been developed in the past. However, it was not possible to provide an emotional analysis of people in a live speech. Today, the development of artificial intelligence and the high performance of deep learning methods bring studies on live data to the fore. This study aims to detect emotions in the human voice using artificial intelligence methods. One of the most important requirements of artificial intelligence works is data. The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) open-source dataset was used in the study. The RAVDESS dataset contains more than 2000 data recorded as speeches and songs by 24 actors. Data were collected for eight different moods from the actors. It was aimed at detecting eight different emotion classes, including neutral, calm, happy, sad, angry, fearful, disgusted, and surprised moods. The multilayer perceptron (MLP) classifier, a widely used supervised learning algorithm, was preferred for classification. The proposed model's performance was compared with that of similar studies, and the results were evaluated. An overall accuracy of 81% was obtained for classifying eight different emotions by using the proposed model on the RAVDESS dataset. |
format | Online Article Text |
id | pubmed-8979705 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-89797052022-04-05 Human-Computer Interaction for Recognizing Speech Emotions Using Multilayer Perceptron Classifier Alnuaim, Abeer Ali Zakariah, Mohammed Shukla, Prashant Kumar Alhadlaq, Aseel Hatamleh, Wesam Atef Tarazi, Hussam Sureshbabu, R. Ratna, Rajnish J Healthc Eng Research Article Human-computer interaction (HCI) has seen a paradigm shift from textual or display-based control toward more intuitive control modalities such as voice, gesture, and mimicry. Particularly, speech has a great deal of information, conveying information about the speaker's inner condition and his/her aim and desire. While word analysis enables the speaker's request to be understood, other speech features disclose the speaker's mood, purpose, and motive. As a result, emotion recognition from speech has become critical in current human-computer interaction systems. Moreover, the findings of the several professions involved in emotion recognition are difficult to combine. Many sound analysis methods have been developed in the past. However, it was not possible to provide an emotional analysis of people in a live speech. Today, the development of artificial intelligence and the high performance of deep learning methods bring studies on live data to the fore. This study aims to detect emotions in the human voice using artificial intelligence methods. One of the most important requirements of artificial intelligence works is data. The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) open-source dataset was used in the study. The RAVDESS dataset contains more than 2000 data recorded as speeches and songs by 24 actors. Data were collected for eight different moods from the actors. It was aimed at detecting eight different emotion classes, including neutral, calm, happy, sad, angry, fearful, disgusted, and surprised moods. The multilayer perceptron (MLP) classifier, a widely used supervised learning algorithm, was preferred for classification. The proposed model's performance was compared with that of similar studies, and the results were evaluated. An overall accuracy of 81% was obtained for classifying eight different emotions by using the proposed model on the RAVDESS dataset. Hindawi 2022-03-28 /pmc/articles/PMC8979705/ /pubmed/35388315 http://dx.doi.org/10.1155/2022/6005446 Text en Copyright © 2022 Abeer Ali Alnuaim et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Alnuaim, Abeer Ali Zakariah, Mohammed Shukla, Prashant Kumar Alhadlaq, Aseel Hatamleh, Wesam Atef Tarazi, Hussam Sureshbabu, R. Ratna, Rajnish Human-Computer Interaction for Recognizing Speech Emotions Using Multilayer Perceptron Classifier |
title | Human-Computer Interaction for Recognizing Speech Emotions Using Multilayer Perceptron Classifier |
title_full | Human-Computer Interaction for Recognizing Speech Emotions Using Multilayer Perceptron Classifier |
title_fullStr | Human-Computer Interaction for Recognizing Speech Emotions Using Multilayer Perceptron Classifier |
title_full_unstemmed | Human-Computer Interaction for Recognizing Speech Emotions Using Multilayer Perceptron Classifier |
title_short | Human-Computer Interaction for Recognizing Speech Emotions Using Multilayer Perceptron Classifier |
title_sort | human-computer interaction for recognizing speech emotions using multilayer perceptron classifier |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8979705/ https://www.ncbi.nlm.nih.gov/pubmed/35388315 http://dx.doi.org/10.1155/2022/6005446 |
work_keys_str_mv | AT alnuaimabeerali humancomputerinteractionforrecognizingspeechemotionsusingmultilayerperceptronclassifier AT zakariahmohammed humancomputerinteractionforrecognizingspeechemotionsusingmultilayerperceptronclassifier AT shuklaprashantkumar humancomputerinteractionforrecognizingspeechemotionsusingmultilayerperceptronclassifier AT alhadlaqaseel humancomputerinteractionforrecognizingspeechemotionsusingmultilayerperceptronclassifier AT hatamlehwesamatef humancomputerinteractionforrecognizingspeechemotionsusingmultilayerperceptronclassifier AT tarazihussam humancomputerinteractionforrecognizingspeechemotionsusingmultilayerperceptronclassifier AT sureshbabur humancomputerinteractionforrecognizingspeechemotionsusingmultilayerperceptronclassifier AT ratnarajnish humancomputerinteractionforrecognizingspeechemotionsusingmultilayerperceptronclassifier |