Characteristics of human auditory model based on compensation of glottal features in speech emotion recognition

2018 ◽  
Vol 81 ◽  
pp. 291-296 ◽  
Author(s):  
Sun Ying ◽  
Zhang Xue-Ying
Keyword(s):  
Emotion Recognition ◽  
Speech Emotion Recognition ◽  
Auditory Model ◽  
Model Based ◽  
Glottal Features

Speech emotion recognition model based on Bi-GRU and Focal Loss

2020 ◽  
Vol 140 ◽  
pp. 358-365
Author(s):  
Zijiang Zhu ◽  
Weihuang Dai ◽  
Yi Hu ◽  
Junshan Li
Keyword(s):  
Emotion Recognition ◽  
Speech Emotion Recognition ◽  
Recognition Model ◽  
Model Based

Affective Model Based Speech Emotion Recognition Using Deep Learning Techniques

2020 ◽  
Vol 5 (4&5) ◽  
pp. 9
Author(s):  
D. Karthika Renuka ◽  
C. Akalya Devi ◽  
R. Kiruba Tharani ◽  
G. Pooventhiran
Keyword(s):  
Deep Learning ◽  
Emotion Recognition ◽  
Speech Emotion Recognition ◽  
Model Based ◽  
Learning Techniques

Simulation of English speech emotion recognition based on transfer learning and CNN neural network

2020 ◽  
pp. 1-12
Author(s):  
Xuehua Chen
Keyword(s):  
Neural Network ◽  
Emotion Recognition ◽  
Transfer Learning ◽  
Speech Emotion Recognition ◽  
Training Strategy ◽  
Model Based ◽  
Time Translation ◽  
The Difference ◽  
Statistical Graph ◽  
Weight Transfer

The difference between English and Chinese expressions is that English emphasizes the stress of syllables, so the recognition of English speech emotions plays an important role in learning English. This study uses transfer learning as the technical support to study English speech emotion recognition. The acoustic model based on weight transfer has two different training strategies: single-stage training and two-stage training strategy. By comparing the performance of the English speech emotion recognition model based on CNN neural network and the model proposed in this paper, the statistical comparison data is drawn into a statistical graph. The research results show that transfer learning has certain advantages over other algorithms in English speech emotion recognition. In the subsequent teaching and real-time translation equipment research, transfer learning can be applied to English models.

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