A novel speech emotion recognition algorithm based on wavelet kernel sparse classifier in stacked deep auto-encoder model

2019 ◽  
Vol 23 (3-4) ◽  
pp. 521-529 ◽  
Author(s):  
Pengcheng Wei ◽  
Yu Zhao
Keyword(s):  
Emotion Recognition ◽  
Recognition Algorithm ◽  
Speech Emotion Recognition

Deep Learning based Speech Emotion Recognition System

2021 ◽  
Vol 23 (12) ◽  
pp. 212-223
Author(s):  
P Jothi Thilaga ◽  
◽  
S Kavipriya ◽  
K Vijayalakshmi ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Emotion Recognition ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Speech Emotion Recognition ◽  
Natural Interaction ◽  
Everyday Activities ◽  
Verbal Content ◽  
Voice Interaction

Emotions are elementary for humans, impacting perception and everyday activities like communication, learning and decision-making. Speech emotion Recognition (SER) systems aim to facilitate the natural interaction with machines by direct voice interaction rather than exploitation ancient devices as input to know verbal content and build it straightforward for human listeners to react. During this SER system primarily composed of 2 sections called feature extraction and feature classification phase. SER implements on bots to speak with humans during a non-lexical manner. The speech emotion recognition algorithm here is predicated on the Convolutional Neural Network (CNN) model, which uses varied modules for emotion recognition and classifiers to differentiate feelings like happiness, calm, anger, neutral state, sadness, and fear. The accomplishment of classification is predicated on extracted features. Finally, the emotion of a speech signal will be determined.

Speech emotion recognition algorithm based on modified SVM

2013 ◽  
Vol 33 (7) ◽  
pp. 1938-1941 ◽  
Author(s):  
Shuling LI ◽  
Rong LIU ◽  
Liuqin ZHANG ◽  
Hong LIU
Keyword(s):  
Emotion Recognition ◽  
Recognition Algorithm ◽  
Speech Emotion Recognition

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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