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.
[1] Rabiner L R. A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proceedings of the IEEE, 1989, 77(2): 257-286 [2] Linde Y, Buzo A, Gray R M. An Algorithm for Vector Quantizer Design. IEEE Trans on Communications, 1980, 28(1): 84-95 [3] Soong F K, Rosenberg A E, Rabiner L R, et al. A Vector Quantization Approach to Speaker Recognition // Proc of the IEEE International Conference on Acoustics, Speech, and Signal Processing. Tampa, USA, 1985: 387-390 [4] Yen C C, Shen C Y, Chen M S. A Two-Phase Hybrid Codebook Generation Technique for Vector Quantization // Proc of the 17th IEEE International Conference on Image Processing. Hong Kong, China, 2010: 4309-4312 [5] Yuan Y J, Zhou Q, Zhao P H. Vector Quantization Codebook Design Method for Speech Recognition Based on Genetic Algorithm // Proc of the 2nd International Conference on Information Engineering and Computer Science. Wuhan, China, 2010: 1-4 [6] Li X J, Xu L P, Yang L. An Improved Particle Swarm Optimization Algorithm for Vector Quantization. Pattern Recognition and Artificial Intelligence, 2008, 21(3): 285-289 (in Chinese) (李小捷,许录平,杨 莉.应用于矢量量化的改进粒子群优化算法.模式识别与人工智能, 2008, 21(3): 285-289) [7] Li X, Luo X H, Zhang J H. Codebook Design for Image Vector Quantization with Ant Colony Optimization. Acta Electronica Sinica, 2004, 32(7): 1082-1085 (in Chinese) (李 霞,罗雪晖,张基宏.基于人工蚁群优化的矢量量化码书设计算法.电子学报, 2004, 32(7): 1082-1085) [8] Chu S C, Tsai P W, Pan J S. Cat Swarm Optimization // Proc of the 9th Pacific Rim International Conference on Artificial Intelligence. Guilin, China, 2006: 854-588 [9] Chu S C, Tsai P W. Computational Intelligence Based on the Behavior of Cats. International Journal of Innovative Computing, Information and Control, 2007, 3(1): 163-173 [10] Tsai P W, Pan J S, Chen S M, et al. Parallel Cat Swarm Optimization // Proc of the International Conference on Machine Learning and Cybernetics. Kunming, China, 2008, VI: 3328-3333 [11] Liu R C, Shen Z C, Jiao L C, et al. Immunodomaince Based Clonal Selection Clustering Algorithm // Proc of the IEEE Congress on Evolutionary Computation. Barcelona, Spain, 2010: 1-7 [12] Yan X H. An Artificial Immune-Based Intrusion Detection Model Using Vaccination Strategy. Acta Electronica Sinica, 2009, 37(4): 780-785 (in Chinese) (严宣辉.应用疫苗接种策略的免疫入侵检测模型.电子学报, 2009, 37(4): 780-785) [13] Yang S Y, Zhang H. Swarm Intelligence and Bionic Calculation-MATLAB Implementation. Beijing, China: Publishing House of Electronics Industry, 2012: 186-189 (in Chinese) (杨淑莹,张 桦.群体智能与仿生计算——MATLAB技术实现.北京:电子工业出版社, 2012: 186-189)
2014, Vol. 27 
