MFCCs and TEO-MFCCs for Stress Detection on Women Gender through Deep Learning Analysis-Reference-Cited by-同舟云学术

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MFCCs and TEO-MFCCs for Stress Detection on Women Gender through Deep Learning Analysis

Published:2023-08-15 Issue: Volume: Page:
ISSN:
Container-title:2023 9th International Conference on Computer and Communication Engineering (ICCCE)
language:
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Author:

Zainal Nur Aishah¹,Asnawi Ani Liza¹,Jusoh Ahmad Zamani¹,Ibrahim Siti Noorjannah¹,Ramli Huda Adibah Mohd¹,Azmin Nor Fadhillah Mohamed²

Affiliation:

1. International Islamic University Malaysia,Kuliyyah of Engineering,Department of Electrical Engineering,Kuala Lumpur,Malaysia

2. International Islamic University Malaysia,Kuliyyah of Engineering,Department of Chemical Engineering and Sustainability,Kuala Lumpur,Malaysia

Funder

Ministry of Higher Education

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10246011/10246027/10246098.pdf?arnumber=10246098

Reference27 articles.

1. Fusing traditionally extracted features with deep learned features from the speech spectrogram for anger and stress detection using convolution neural network

2. Analysis of Stressed Speech on Teager Energy Operator (TEO);jena,2018

3. Automated speech-based screening of depression using deep convolutional neural networks

4. Speech analysis for health: Current state-of-the-art and the increasing impact of deep learning

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Enhancing Stress Speech Classification Through the Fusion of Emotional Datasets Utilizing MFCCs with CNN;2024 9th International Conference on Mechatronics Engineering (ICOM);2024-08-13

2. Integration of MFCCs and CNN for Multi-Class Stress Speech Classification on Unscripted Dataset;IIUM Engineering Journal;2024-07-14

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