Neural networks: algorithms, applications, and programming techniques
Neural networks: algorithms, applications, and programming techniques
Practical neural network recipes in C++
Practical neural network recipes in C++
The roots of backpropagation: from ordered derivatives to neural networks and political forecasting
The roots of backpropagation: from ordered derivatives to neural networks and political forecasting
Neural Networks for Optimization and Signal Processing
Neural Networks for Optimization and Signal Processing
Comparison between Fuzzy and NN Method for Speech Emotion Recognition
ICITA '05 Proceedings of the Third International Conference on Information Technology and Applications (ICITA'05) Volume 2 - Volume 02
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Speech and emotion recognition improve the quality of human computer interaction and allow more easy to use interfaces for every level of user in software applications. In this study, we have developed the emotion recognition neural network (ERNN) 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 97932 training sets is used to train the ERNN. A new set of 24483 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.