Abstract: In this paper, a self-adaptive fuzzy c-means clustering algorithm is proposed. It is designed to overcome the shortcomings of the traditional LBG vector-quantization algorithm. Experimental results on MFCC quantization show that the proposed algorithm has similar quantization error compared to the classic LBG algorithm while significantly reducing codebook size and memory footprint.

Key words: LBG algorithm, self-adaption, Mel Frequency Cepstrum Coefficient（MFCC）, fuzzy clustering, Vector Quantization（VQ）

摘要： 采用模糊聚类C均值聚类确定型心改进LBG算法，实现语音参数MFCC码本的矢量量化，实验结果表明，该算法有着与单一LBG算法相近的量化误差，自适应确定码本大小码，码本尺寸显著降低，减小码本的存储量。

关键词: LBG算法, 自适应, 梅尔频率倒谱系数（MFCC）, 模糊聚类, 矢量量化