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模糊C-均值聚类新算法在说话人辨认中的应用
引用本文:王成儒,王金甲.模糊C-均值聚类新算法在说话人辨认中的应用[J].计算机工程与应用,2003,39(27):94-95,140.
作者姓名:王成儒  王金甲
作者单位:燕山大学通信与电子工程系,秦皇岛,066004
摘    要:该文提出了一种将模糊C-均值聚类法的各种改进算法与矢量量化法相结合的说话人辨认的新方法。首先从语音信号中提取MFCC特征矢量,其次利用矢量量化来设计码书,最后用改进算法对待识语音进行辨认。新算法的辨认率达到95%以上,抗噪性能也优于矢量量化法。

关 键 词:模糊C-均值聚类法  矢量量化  模拟退火算法  遗传算法  进化免疫算法
文章编号:1002-8331-(2003)27-0094-02

Novel Algorithms of Fuzzy C-mean Clustering for Speaker Identification
Wang Chengru Wang Jinjia.Novel Algorithms of Fuzzy C-mean Clustering for Speaker Identification[J].Computer Engineering and Applications,2003,39(27):94-95,140.
Authors:Wang Chengru Wang Jinjia
Abstract:Several new algorithms of fuzzy C-mean clustering with the combination of vector quantization are proposed for speaker identification.First the Mel -frequency Cepstral Coefficients are extracted from speech signals.Second,codebooks are designed using vector quantization approach.At last,someone's speeches are identified using the new algorithms of FCM.It is proved that identification rate of the algorithms is more than95%and robust of them is super to vector quantization approach.
Keywords:Fuzzy C-mean clustering  Vector quantization  Simulated annealing algorithm  Genetic algorithm  Immune evolu-tionary algorithm
本文献已被 CNKI 维普 万方数据 等数据库收录!
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