Multi-classification speech emotion recognition based on two-stage bottleneck features selection and MCJD algorithm

2022 ◽  
Author(s):  
Linhui Sun ◽  
Yiqing Huang ◽  
Qiu Li ◽  
Pingan Li
Keyword(s):  
Emotion Recognition ◽  
Speech Emotion Recognition ◽  
Features Selection ◽  
Two Stage ◽  
Multi Classification

scholarly journals IMPROVED SPEAKER-INDEPENDENT EMOTION RECOGNITION FROM SPEECH USING TWO-STAGE FEATURE REDUCTION

2015 ◽  
Author(s):  
Hasrul Mohd Nazid ◽  
Hariharan Muthusamy ◽  
Vikneswaran Vijean ◽  
Sazali Yaacob
Keyword(s):  
Emotion Recognition ◽  
Principal Component ◽  
Feature Reduction ◽  
Speech Emotion Recognition ◽  
Emotional Speech ◽  
Two Stage ◽  
Linear Discriminant ◽  
Speaker Independent ◽  
Speech Features ◽  
And Gender

In the recent years, researchers are focusing to improve the accuracy of speech emotion recognition. Generally, high emotion recognition accuracies were obtained for two-class emotion recognition, but multi-class emotion recognition is still a challenging task . The main aim of this work is to propose a two-stage feature reduction using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for improving the accuracy of the speech emotion recognition (ER) system. Short-term speech features were extracted from the emotional speech signals. Experiments were carried out using four different supervised classifi ers with two different emotional speech databases. From the experimental results, it can be inferred that the proposed method provides better accuracies of 87.48% for speaker dependent (SD) and gender dependent (GD) ER experiment, 85.15% for speaker independent (SI) ER experiment, and 87.09% for gender independent (GI) experiment.  

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 A Two-Stage Attention Based Modality Fusion Framework for Multi-Modal Speech Emotion Recognition

2021 ◽  
Vol E104.D (8) ◽  
pp. 1391-1394
Author(s):  
Dongni HU ◽  
Chengxin CHEN ◽  
Pengyuan ZHANG ◽  
Junfeng LI ◽  
Yonghong YAN ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Speech Emotion Recognition ◽  
Two Stage ◽  
Fusion Framework

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 Based on Sparse Representation

2013 ◽  
Vol 38 (4) ◽  
pp. 465-470 ◽  
Author(s):  
Jingjie Yan ◽  
Xiaolan Wang ◽  
Weiyi Gu ◽  
LiLi Ma
Keyword(s):  
Dimensionality Reduction ◽  
Emotion Recognition ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Speech Emotion Recognition ◽  
Least Squares Regression ◽  
Computer Science Pedagogy ◽  
Reduction Methods ◽  
Analysis Computer

Abstract Speech emotion recognition is deemed to be a meaningful and intractable issue among a number of do- mains comprising sentiment analysis, computer science, pedagogy, and so on. In this study, we investigate speech emotion recognition based on sparse partial least squares regression (SPLSR) approach in depth. We make use of the sparse partial least squares regression method to implement the feature selection and dimensionality reduction on the whole acquired speech emotion features. By the means of exploiting the SPLSR method, the component parts of those redundant and meaningless speech emotion features are lessened to zero while those serviceable and informative speech emotion features are maintained and selected to the following classification step. A number of tests on Berlin database reveal that the recogni- tion rate of the SPLSR method can reach up to 79.23% and is superior to other compared dimensionality reduction methods.

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