关键词: 浊音隶属度, 模糊聚类, 矢量量化, F-LBG算法, 声码器

Abstract:

In order to overcome the defect that the decoding speech was unnatural caused by hard decision and crude judgment,5-dimensional voiced membership vector which was used to describe the information of excitation accurately,was brought forward on the basis of analyzing and comparing the algorithm which recently the mainstream vocoder used to extract and quantify the parameters of the excitation signal,and introducing the concept,membership,from fuzzy mathematics;described the extracting algorithm of voiced membership vector and brought forward the VQ(Vector Quantization) codebook of fuzzy clustering and the LBG cascade training algorithm;established the training sample set of voiced membership codebook by the means of using the extracting algorithm,and trained the codebook of voiced membership by the means of using F-LBG(Fuzzy LBG) algorithm.The results indicate that this algorithm,using voiced membership vector to describe and synthesize the excitation signal,has a very high accuracy and strong noise robustness when it was used to simulate the speech coding and decoding in the area of sinusoidal excitation vocoder,mixed-excitation vocoder and the homomorphic vocoder.

Key words: voiced membership, fuzzy clustering, vector quantization(VQ), F-LBG algorithm, vocoder