Temporal Attention Convolutional Network for Speech Emotion Recognition with Latent Representation

2020 ◽  
Author(s):  
Jiaxing Liu ◽  
Zhilei Liu ◽  
Longbiao Wang ◽  
Yuan Gao ◽  
Lili Guo ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Speech Emotion Recognition ◽  
Temporal Attention ◽  
Convolutional Network

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.

Interaction and Transition Model for Speech Emotion Recognition in Dialogue

2017 ◽  
Author(s):  
Ruo Zhang ◽  
Ando Atsushi ◽  
Satoshi Kobashikawa ◽  
Yushi Aono
Keyword(s):  
Emotion Recognition ◽  
Speech Emotion Recognition ◽  
Transition Model
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