International Journal of Information and Communication Technology Research
مجله بین المللی ارتباطات و فناوری اطلاعات
International Journal of Information and Communication Technology Research
Engineering & Technology
http://ijict.itrc.ac.ir
1
admin
2251-6107
2783-4425
doi
1652
25391
en
jalali
1393
12
1
gregorian
2015
3
1
7
1
online
1
fulltext
fa
Connection Optimization of a Neural Emotion Classifier Using Hybrid Gravitational Search Algorithms
فناوری اطلاعات
Information Technology
پژوهشي
Research
Artificial neural network is an efficient model in pattern recognition applications, but its performance is heavily dependent on using suitable structure and connection weights. This paper presents a hybrid heuristic method for obtaining the optimal weight set and architecture of a feedforward neural emotion classifier based on Gravitational Search Algorithm (GSA) and its binary version (BGSA), respectively. By considering various features of speech signal and concatenating them to a principal feature vector, which includes frame-based Mel frequency cepstral coefficients and energy, a rich medium-size feature set is constructed. The performance of the proposed hybrid GSA-BGSA-neural model is compared with the hybrid of Particle Swarm Optimization (PSO) algorithm and its binary version (BPSO) used for such optimizations. In addition, other models such as GSA-neural hybrid and PSO-neural hybrid are also included in the performance comparisons. Experimental results show that the GSA-optimized models can obtain better results using a lighter network structure.
emotion recognition, speech processing, neural network, connection optimization, structure optimization, gravitational search algorithm
41
51
http://ijict.itrc.ac.ir/browse.php?a_code=A-10-27-82&slc_lang=fa&sid=1
Mansour
Sheikhan
1003194753284600260
1003194753284600260
Yes
Mahdi
Abbasnezhad Arabi
1003194753284600261
1003194753284600261
No
Davood
Gharavian
1003194753284600262
1003194753284600262
No