Fundamental Research Grant Scheme (FRGS) - FRGS19-076-0684, Speech Emotion Recognition and Depression Prediction Based on Speech Analysis using Deep Neural Networks
Speech signals contain a lot of information that can be used by computers to gain insight into a user's state, such as emotion recognition and depression prediction. Numerous applications exist, ranging from customer service to depression prevention. We propose several deep-learning-based metho...
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my.iium.irep.968542022-02-21T07:06:21Z http://irep.iium.edu.my/96854/ Fundamental Research Grant Scheme (FRGS) - FRGS19-076-0684, Speech Emotion Recognition and Depression Prediction Based on Speech Analysis using Deep Neural Networks Gunawan, Teddy Surya Draman, Samsul Kartiwi, Mira Borhan, Lihanna Abdul Malik, Noreha Abdul Rahman, Farah Diyana Elsheikh, Elsheikh Mohamed Ahmed Alghifari, Muhammad Fahreza Ahmad Qadri, Syed Asif Ashraf, Arselan Wani, Taiba Majid TK7885 Computer engineering Speech signals contain a lot of information that can be used by computers to gain insight into a user's state, such as emotion recognition and depression prediction. Numerous applications exist, ranging from customer service to depression prevention. We propose several deep-learning-based methodologies for detecting emotion and depression in this research. We used variants of deep neural networks such as deep feedforward networks and convolutional networks. The deep learning model was trained using well-known databases such as the Berlin Emotion Database and the DAIC-WOZ Depression Dataset. The algorithm achieves an accuracy of 80.5 percent for speech emotion recognition across four languages: English, German, French, and Italian. The current algorithm detects depression with a 60.1 percent accuracy when tested on the DAIC-WOZ dataset. Additionally, this research resulted in the creation of the Sorrow Analysis Dataset – an English depression audio dataset comprised of 64 distinct samples of depressed and non-depressed individuals. Further validation using 1-dimensional convolutional networks resulted in an average accuracy of 97 percent. Further research could be conducted using other deep learning architectures, other datasets, and implementation on edge computing. 2022-02-05 Monograph NonPeerReviewed application/pdf en http://irep.iium.edu.my/96854/1/GunawanFRGS19-076-0684FinalReportFeb22.pdf Gunawan, Teddy Surya and Draman, Samsul and Kartiwi, Mira and Borhan, Lihanna and Abdul Malik, Noreha and Abdul Rahman, Farah Diyana and Elsheikh, Elsheikh Mohamed Ahmed and Alghifari, Muhammad Fahreza and Ahmad Qadri, Syed Asif and Ashraf, Arselan and Wani, Taiba Majid (2022) Fundamental Research Grant Scheme (FRGS) - FRGS19-076-0684, Speech Emotion Recognition and Depression Prediction Based on Speech Analysis using Deep Neural Networks. Technical Report. UNSPECIFIED. (Unpublished) |
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TK7885 Computer engineering Gunawan, Teddy Surya Draman, Samsul Kartiwi, Mira Borhan, Lihanna Abdul Malik, Noreha Abdul Rahman, Farah Diyana Elsheikh, Elsheikh Mohamed Ahmed Alghifari, Muhammad Fahreza Ahmad Qadri, Syed Asif Ashraf, Arselan Wani, Taiba Majid Fundamental Research Grant Scheme (FRGS) - FRGS19-076-0684, Speech Emotion Recognition and Depression Prediction Based on Speech Analysis using Deep Neural Networks |
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Speech signals contain a lot of information that can be used by computers to gain insight into a user's state, such as emotion recognition and depression prediction. Numerous applications exist, ranging from customer service to depression prevention. We propose several deep-learning-based methodologies for detecting emotion and depression in this research. We used variants of deep neural networks such as deep feedforward networks and convolutional networks. The deep learning model was trained using well-known databases such as the Berlin Emotion Database and the DAIC-WOZ Depression Dataset. The algorithm achieves an accuracy of 80.5 percent for speech emotion recognition across four languages: English, German, French, and Italian. The current algorithm detects depression with a 60.1 percent accuracy when tested on the DAIC-WOZ dataset. Additionally, this research resulted in the creation of the Sorrow Analysis Dataset – an English depression audio dataset comprised of 64 distinct samples of depressed and non-depressed individuals. Further validation using 1-dimensional convolutional networks resulted in an average accuracy of 97 percent. Further research could be conducted using other deep learning architectures, other datasets, and implementation on edge computing. |
format |
Monograph |
author |
Gunawan, Teddy Surya Draman, Samsul Kartiwi, Mira Borhan, Lihanna Abdul Malik, Noreha Abdul Rahman, Farah Diyana Elsheikh, Elsheikh Mohamed Ahmed Alghifari, Muhammad Fahreza Ahmad Qadri, Syed Asif Ashraf, Arselan Wani, Taiba Majid |
author_facet |
Gunawan, Teddy Surya Draman, Samsul Kartiwi, Mira Borhan, Lihanna Abdul Malik, Noreha Abdul Rahman, Farah Diyana Elsheikh, Elsheikh Mohamed Ahmed Alghifari, Muhammad Fahreza Ahmad Qadri, Syed Asif Ashraf, Arselan Wani, Taiba Majid |
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Gunawan, Teddy Surya |
title |
Fundamental Research Grant Scheme (FRGS) - FRGS19-076-0684, Speech Emotion Recognition and Depression Prediction Based on Speech Analysis using Deep Neural Networks |
title_short |
Fundamental Research Grant Scheme (FRGS) - FRGS19-076-0684, Speech Emotion Recognition and Depression Prediction Based on Speech Analysis using Deep Neural Networks |
title_full |
Fundamental Research Grant Scheme (FRGS) - FRGS19-076-0684, Speech Emotion Recognition and Depression Prediction Based on Speech Analysis using Deep Neural Networks |
title_fullStr |
Fundamental Research Grant Scheme (FRGS) - FRGS19-076-0684, Speech Emotion Recognition and Depression Prediction Based on Speech Analysis using Deep Neural Networks |
title_full_unstemmed |
Fundamental Research Grant Scheme (FRGS) - FRGS19-076-0684, Speech Emotion Recognition and Depression Prediction Based on Speech Analysis using Deep Neural Networks |
title_sort |
fundamental research grant scheme (frgs) - frgs19-076-0684, speech emotion recognition and depression prediction based on speech analysis using deep neural networks |
publishDate |
2022 |
url |
http://irep.iium.edu.my/96854/1/GunawanFRGS19-076-0684FinalReportFeb22.pdf http://irep.iium.edu.my/96854/ |
_version_ |
1725972482077229056 |
score |
13.160551 |