Automatic Recognition of Emotions and Actions in Bi-modal Video Analysis

2015 ◽  
pp. 427-438
Author(s):  
Chenjian Wu ◽  
Chengwei Huang ◽  
Hong Chen
Keyword(s):  
Video Analysis ◽  
Automatic Recognition ◽  
Recognition Of Emotions

Formal Protocol for the Creation of a Database of Physiological and Behavioral Signals for the Automatic Recognition of Emotions

2019 ◽  
pp. 211-226
Author(s):  
Yesenia N. González-Meneses ◽  
Josefina Guerrero-García ◽  
Carlos Alberto Reyes-García ◽  
Ivan Olmos-Pineda ◽  
Juan Manuel González-Calleros
Keyword(s):  
Automatic Recognition ◽  
The Creation ◽  
Recognition Of Emotions

scholarly journals Applying Multitask Deep Learning to Emotion Recognition in Speech

2021 ◽  
Vol 25 (1) ◽  
pp. 82-109
Author(s):  
A. V. Ryabinov ◽  
M. Yu. Uzdiaev ◽  
I. V. Vatamaniuk
Keyword(s):  
Emotion Recognition ◽  
Automatic Recognition ◽  
Multitask Learning ◽  
Security Systems ◽  
Simultaneous Reduction ◽  
Proposed Model ◽  
Recognition Of Emotions ◽  
Low Efficiency ◽  
First Time

Purpose of research. Emotions play one of the key roles in the regulation of human behaviour. Solving the problem of automatic recognition of emotions makes it possible to increase the effectiveness of operation of a whole range of digital systems such as security systems, human-machine interfaces, e-commerce systems, etc. At the same time, the low efficiency of modern approaches to recognizing emotions in speech can be noted. This work studies automatic recognition of emotions in speech applying machine learning methods.Methods. The article describes and tests an approach to automatic recognition of emotions in speech based on multitask learning of deep convolution neural networks of AlexNet and VGG architectures using automatic selection of the weight coefficients for each task when calculating the final loss value during learning. All the models were trained on a sample of the IEMOCAP dataset with four emotional categories of ‘anger’, ‘happiness’, ‘neutral emotion’, ‘sadness’. The log-mel spectrograms of statements processed by a specialized algorithm are used as input data.Results. The considered models were tested on the basis of numerical metrics: the share of correctly recognized instances, accuracy, completeness, f-measure. For all of the above metrics, an improvement in the quality of emotion recognition by the proposed model was obtained in comparison with the two basic single-task models as well as with known solutions. This result is achieved through the use of automatic weighting of the values of the loss functions from individual tasks when forming the final value of the error in the learning process.Conclusion. The resulting improvement in the quality of emotion recognition in comparison with the known solutions confirms the feasibility of applying multitask learning to increase the accuracy of emotion recognition models. The developed approach makes it possible to achieve a uniform and simultaneous reduction of errors of individual tasks, and is used in the field of emotions recognition in speech for the first time.

scholarly journals Automatic Recognition of Emotions from Speech

2019 ◽  
Vol 10 (4) ◽  
pp. 101
Author(s):  
Martin Gjoreski ◽  
Hristijan Gjoreski ◽  
Andrea Kulakov
Keyword(s):  
Automatic Recognition ◽  
Recognition Of Emotions

Nonlinear Methodologies Applied to Automatic Recognition of Emotions: An EEG Review

2017 ◽  
pp. 754-765 ◽  
Author(s):  
Beatriz García-Martínez ◽  
Arturo Martínez-Rodrigo ◽  
Raúl Alcaraz ◽  
Antonio Fernández-Caballero ◽  
Pascual González
Keyword(s):  
Automatic Recognition ◽  
Recognition Of Emotions
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