关键词: 模糊熵, 样本熵, 语音端点检测, 模糊C均值聚类算法, 贝叶斯信息准则

Abstract: In order to improve the accuracy of speech endpoint detection in car environment, introduces a new measure of time series complexity, fuzzy entropy, and applies it to the characterization of speech. With sample entropy and fuzzy entropy respectively to the characterization of speech signals in car environments, and uses fuzzy C-means clustering algorithm and Bayesian information criterion algorithm, estimates the thresholds of the characteristics, then by using dual threshold method for endpoint detection. Experimental results demonstrate that, the fuzzy entropy method can distinguish the noise and speech signals better and has better performance of endpoint detection than sample entropy in car environments, the accuracy rate of fuzzy entropy method is superior to sample entropy method more than 16% in the same environments.

Key words: fuzzy entropy, sample entropy, speech endpoint detection, fuzzy C-means clustering algorithm, Bayesian Information Criterions