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01-Applied Mathematics & Information Sciences
An International Journal
               
 
 
 
 
 
 
 
 
 
 
 
 
 

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Volumes > Volume 19 > No. 1

 
   

Exploring Deep Learning Methods for Audio Speech Emotion Detection: An Ensemble MFCCs, CNNs and LSTM

PP: 75-85
doi:10.18576/amis/190107
Author(s)
Shaik Abdul Khalandar Basha, P. M. Durai Raj Vincent, Suleiman Ibrahim Mohammad, Asokan Vasudevan, Eddie Eu Hui Soon, Qusai Shambour, Muhammad Turki Alshurideh,
Abstract
Our world relies entirely on the gadgets we use every day, making the world heavily materialized.The human-machine interactions that are currently available are not supported under line-of-sight (LOS). The proposed emotional communication is based on non-line-of-sight (NLOS) to break away from Conventional human-machine interactions. This emotional communication is defined as interactive, similar to the usual video and voice media we use daily; similarly, the information is transmitted over long distances. We proposed the EAS framework, another ensemble technique for an emotional communication protocol for real-time communication requirements. This framework supports the communication of emotional realization. They also designed. Finally, which are developing CNN-LSTM architectures for feature extraction, implementing an attention mechanism for selecting relevant features, creating for selecting relevant features, and creating for real-time scenarios, performance-evaluated matrices are applied CNN-LSTM networks with and without attention mechanisms. DCCA feature extraction is used to extract attributes and find correlations among different labels in the dataset. Toanalyze the real-time performance of the process in emotional communication with long-distance communications between others. The proposed CNN-LSTM model achieves the highest accuracy with 87.08% accuracy, while existing models, such as CNN baseline and LSTM models, showed 81.11% and 84.01%, respectively. Our approach shows improved Accuracy compared to existing works, especially for real-time applications.

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