Pattern recognition and features selection for speech emotion recognition model using deep learning

2020 ◽  
Vol 23 (4) ◽  
pp. 799-806
Author(s):  
Kittisak Jermsittiparsert ◽  
Abdurrahman Abdurrahman ◽  
Parinya Siriattakul ◽  
Ludmila A. Sundeeva ◽  
Wahidah Hashim ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Deep Learning ◽  
Emotion Recognition ◽  
Speech Emotion Recognition ◽  
Features Selection ◽  
Recognition Model ◽  
Selection For

scholarly journals Pattern Recognition Methods and Features Selection for Speech Emotion Recognition System

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Pavol Partila ◽  
Miroslav Voznak ◽  
Jaromir Tovarek
Keyword(s):  
Emotion Recognition ◽  
Voice Quality ◽  
Gaussian Mixture ◽  
Recognition System ◽  
Speech Emotion Recognition ◽  
Features Selection ◽  
Pattern Recognition Methods ◽  
Selection For ◽  
Combination Of Methods ◽  
The Impact

The impact of the classification method and features selection for the speech emotion recognition accuracy is discussed in this paper. Selecting the correct parameters in combination with the classifier is an important part of reducing the complexity of system computing. This step is necessary especially for systems that will be deployed in real-time applications. The reason for the development and improvement of speech emotion recognition systems is wide usability in nowadays automatic voice controlled systems. Berlin database of emotional recordings was used in this experiment. Classification accuracy of artificial neural networks,k-nearest neighbours, and Gaussian mixture model is measured considering the selection of prosodic, spectral, and voice quality features. The purpose was to find an optimal combination of methods and group of features for stress detection in human speech. The research contribution lies in the design of the speech emotion recognition system due to its accuracy and efficiency.

Speech Emotion Recognition on Indonesian YouTube Web Series Using Deep Learning Approach

2020 ◽  
Author(s):  
Hanina Nuralifa Zahra ◽  
Muhammad Okky Ibrohim ◽  
Junaedi Fahmi ◽  
Rike Adelia ◽  
Fandy Akhmad Nur Febryanto ◽  
...  
Keyword(s):  
Deep Learning ◽  
Emotion Recognition ◽  
Speech Emotion Recognition ◽  
Learning Approach ◽  
Web Series

scholarly journals Efficient Facial Emotion Recognition Model using Deep Convolutional Neural Network and Modified Joint Trilateral Filter

2021 ◽  
Author(s):  
Naveen Kumari ◽  
Rekha Bhatia
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Emotion Recognition ◽  
Deep Convolutional Neural Network ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Learning Models ◽  
Recognition Model ◽  
Trilateral Filter

Abstract Facial emotion recognition extracts the human emotions from the images and videos. As such, it requires an algorithm to understand and model the relationships between faces and facial expressions, and to recognize human emotions. Recently, deep learning models are extensively utilized enhance the facial emotion recognition rate. However, the deep learning models suffer from the overfitting issue. Moreover, deep learning models perform poorly for images which have poor visibility and noise. Therefore, in this paper, a novel deep learning based facial emotion recognition tool is proposed. Initially, a joint trilateral filter is applied to the obtained dataset to remove the noise. Thereafter, contrast-limited adaptive histogram equalization (CLAHE) is applied to the filtered images to improve the visibility of images. Finally, a deep convolutional neural network is trained. Nadam optimizer is also utilized to optimize the cost function of deep convolutional neural networks. Experiments are achieved by using the benchmark dataset and competitive human emotion recognition models. Comparative analysis demonstrates that the proposed facial emotion recognition model performs considerably better compared to the competitive models.

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