The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON SIGNAL PROCESSING
Transactions ID Number: 29-165
Full Name: Mehmet Unluturk
Position: Assistant Professor
Age: ON
Sex: Male
Address: Sakarya Cad. No: 156 35330 Balcova / Izmir
Country: TURKEY
Tel: (+90)232-488-8255
Tel prefix:
Fax: (+90)232-488-8475
E-mail address: suleyman.unluturk@ieu.edu.tr
Other E-mails: mehmet_unluturk@yahoo.com
Title of the Paper: A Comparison of Neural Networks for Real-Time Emotion Recognition from Speech Signals
Authors as they appear in the Paper: MEHMET S. UNLUTURK, KAYA OGUZ, COSKUN ATAY
Email addresses of all the authors: suleyman.unluturk@ieu.edu.tr, kaya.oguz@ieu.edu.tr, coskun.atay@ieu.edu.tr
Number of paper pages: 10
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 outp!
ut 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.
Keywords: Back propagation learning algorithm, Neural network, Emotion, Speech, Power Spectrum, Fast-Fourier Transform (FFT), Bayes optimal decision rule.
EXTENSION of the file: .doc
Special (Invited) Session: Emotion Recognition Using Neural Networks
Organizer of the Session: 611-252
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