基于LLE和模糊核聚类的语音可视化仿真

Speech Visualization Simulation Based on LLE and Fuzzy Kernel Clustering Algorithm

根据语音信号的时变特性,提出了一种具有很好分类定位能力的语音可视化方法——局部线性嵌入(LLE)和模糊核聚类相结合的算法.通过利用LLE对提取的语音特征进行非线性降维,然后再利用模糊核聚类算法对其进行聚类分析,即利用Mercer核,将原始空间通过非线性映射到高维特征空间,在高维特征空间中对语音信号特征进行模糊核聚类分析.由于经过了核函数的映射,使原来没有显现的特征突现出来,从而能够更好地支持基于位置的语音可视化.以10名男生和10名女生在实验室环境下的720个语音资料(汉语元音)作为样本进行了试验,试验结果验证了该方法的可行性和有效性.

According to the time-varying speech signal, a novel method combining LLE（locally linear embedding） with fuzzy kernel clustering algorithm was proposed for speech visualization, where LLE could reduce the nonlinear dimensionality of the speech features and then the fuzzy kernel clustering algorithm was used for clustering analysis, i.e. the Mercer kernel function was used to change the data in original space into a high-dimensional eigenspace through nonlinear mapping, and then the fuzzy clustering analysis was made in the high-dimensional eigenspace. Thus, after the kernel function mapping, the original inherent features of speech were highlighted to improve the position-based speech visualization. 720 data in Chinese vowels were obtained from 10 male and 10 female students＇ speech in lab, the results of simulation experiments show the feasibility and validity of the method.