TY - JOUR JF - ITRC JO - VL - 7 IS - 1 PY - 2015 Y1 - 2015/3/01 TI - Connection Optimization of a Neural Emotion Classifier Using Hybrid Gravitational Search Algorithms TT - N2 - 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. SP - 41 EP - 51 AU - Sheikhan, Mansour AU - Abbasnezhad Arabi, Mahdi AU - Gharavian, Davood AD - KW - emotion recognition KW - speech processing KW - neural network KW - connection optimization KW - structure optimization KW - gravitational search algorithm UR - http://ijict.itrc.ac.ir/article-1-108-en.html ER -