Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14365/4618
Title: A comparison of neural networks for real-time emotion recognition from speech signals
Authors: Ünlütürk, Mehmet Süleyman
Oguz K.
Atay C.
Keywords: Back propagation learning algorithm
Bayes optimal decision rule
Emotion
Fast-fourier transform (FFT)
Neural network
Power spectrum
Speech
Back propagation learning algorithm
Bayes optimal decision rule
Emotion
Emotion recognition
Fast-fourier transform (FFT)
Hidden neurons
Input node
Optimality
Recognition performance
Software applications
Speech signals
Testing sets
Training sets
Voice recognition
Voice signals
Backpropagation
Backpropagation algorithms
Computer applications
Face recognition
Fast Fourier transforms
Human computer interaction
Interfaces (computer)
Learning algorithms
Learning systems
Neural networks
Neurons
Power spectrum
Speech recognition
Abstract: Speech and emotion recognition improve the quality of human computer interaction and allow easier to use interfaces for every level of user in software applications. In this study, we have developed two different neural networks called emotion recognition neural network (ERNN) and Gram-Charlier emotion recognition neural network (GERNN) to classify the voice signals for emotion recognition. The ERNN has 128 input nodes, 20 hidden neurons, and three summing output nodes. A set of 97920 training sets is used to train the ERNN. A new set of 24480 testing sets is utilized to test the ERNN performance. The samples tested for voice recognition are acquired from the movies " Anger Management" and " Pick of Destiny" . ERNN achieves an average recognition performance of 100%. This high level of recognition suggests that the ERNN is a promising method for emotion recognition in computer applications. Furthermore, the GERNN has four input nodes, 20 hidden neurons, and three output nodes. The GERNN achieves an average recognition performance of 33%. This shows us that we cannot use Gram-Charlier coefficients to discriminate emotion signals. In addition, Hinton diagrams were utilized to display the optimality of ERNN weights.
URI: https://hdl.handle.net/20.500.14365/4618
ISSN: 1790-5022
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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