论文信息 - Emotion recognition using neural networks

Emotion recognition using neural networks

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.

Mehmet S. Unluturk | Kaya Oguz | Coskun Atay

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