Abstract：In the process of codebook design, traditional LBG algorithm is often used for vector quantization which depends on the initial codebook selection and easily falls into local optimum. A vector quantization codebook design method based on immune cat swarm optimization algorithm (ICSO) is proposed to solve the problems.The population is divided into searching group and tracking group. Clonal expansion operator is used for local search in the searching group, and the number of mutation individual is adjusted according to the fitness value. Moreover, dynamic vaccine extraction and vaccination operator are used for global search in the tracking group. The crossover and mutation between individual gene and vaccine make the result close to the optimal solution, and the descendant population is updated through the balance of concentration equilibrium operator and selection operator. Finally, the optimal codebook is obtained from the training vectors by the proposed algorithm and is inputted to the HMM model for training and recognition. The simulation results show that the proposed algorithm does not depend on the selection of initial codebook, has strong robustness and reduces the speech recognition error rate.
杨淑莹，刘旭鹏，陶冲，刘婷婷. 基于免疫猫群优化算法的矢量量化的码书设计及语音识别*[J]. 模式识别与人工智能, 2014, 27(7): 577-583.
YANG Shu-Ying, LIU Xu-Peng, TAO Chong, LIU Ting-Ting. Vector Quantization Codebook Design and Speech Recognition Based on Immune Cat Swarm Optimization Algorithm. , 2014, 27(7): 577-583.
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2014, Vol. 27 
