|
|
Speaker Recognition Method Based on Quantum Logic Cir-cuit Neural Networks |
PAN Ping, LUO Hui, WANG Yang |
College of Computer Science & Information, Guizhou University, Guiyang 550025, China |
|
|
Abstract: Under the same spatial and temporal conditions, the quantum computing is superior to the traditional computing. Because a speaker's speech signal features the time-varying property and the randomness, its characteristic parameters also show high-dimensional characters and large changes in adjacent frames. This paper, based on the quantum information processing theory, takes a frame of the speech signal as a quantum state, and uses quantum logic gate circuits to construct the neural network according to the traditional neural network, and obtains an efficient clustering of the speaker's speech signal. A speaker recognition model is built and a method based on the quantum logic circuit neural network is proposed. This model has a large number of global attractors, and the method can use them to effectively reduce the complexity of the speech signal processing. Through simulations on a classical computer and a comparison with the BP neural network speaker recognition model, it is shown that this method not only can accelerate the convergence rate of the model but also has a better robustness with respect to the parameter changes. The system's recognition rate with the method proposed in this paper is 3.34% in average higher than that with the BP neural network method.
|
Received: 15 April 2013
|
|
|
|
|
[1] |
QIU Guohua, SHENTU Nanying, SHI Zhenglun. Prediction Method of Cement Strength Based on GM-RBF Neural Network Combination Model[J]. journal1, 2014, 32(3): 56-61. |
[2] |
WANG Xinmin, LIU Jixiang, CHEN Qiusong, XIAO Chongchun, WAN Xiaoheng. Optimal Flocculating Sedimentation Parameters of Unclassified Tailings[J]. journal1, 2014, 32(17): 23-28. |
[3] |
ZHANG Hongtao, ZHANG Lingyun, LI Xiaodan, QIU Daoyin. Reactive Power Compensation Based on Radial Basis Function Neural Network for Wind Farm Connected to Power System[J]. journal1, 2014, 32(11): 49-54. |
[4] |
ZHANG Qinli;CHENG Jian;CHEN Qiusong;HU Wei;ZHOU Bihui. Prediction of Backfill Drill-hole Life Based on Combined Model of GA-SVM and Neural Network[J]. , 2013, 31(34): 34-38. |
[5] |
WU Xi;WANG Binbin;ZHOU Hai;YU Jiang;CUI Fang. Dynamic Modification of Super Short Term Numerical Wind Forecast Based on Neural Networks at Wind Farm[J]. , 2013, 31(34): 39-44. |
[6] |
GUAN Xuemei;GUO Minghui;CAO Jun. Predictive Model of Wood Dyeing Pigment Formula[J]. , 2013, 31(17): 29-32. |
|
|
|
|