基于谱熵梅尔积的语音端点检测方法

为了克服传统语音端点检测算法在低信噪比环境下准确率低的问题,提出一种基于谱熵梅尔积（MFPH）的语音端点检测算法.首先,提取带噪语音信号的梅尔频率倒谱系数中的第一维参数MFCC₀,将其与谱熵的乘积作为最终区分语音段和背景噪声段的融合特征参数;然后,结合模糊C均值聚类算法和贝叶斯信息准则（BIC）算法对MFPH特征参数门限值进行自适应估计;最后,采用双门限法进行语音端点检测.实验结果证明,与传统方法比较,该方法在-5~15 dB低信噪比环境下的语音端点检测准确率有较大提高.

Voice Activity Detection Method Based on MFPH

In order to solve the problem that the accuracy of traditional voice activity detection algorithms is low in the low signal-to-noise ratio (SNR) environment,a voice activity detection algorithm based on product of spectral entropy and Mel (MFPH) was proposed. Firstly, the first dimensional parameter MFCC₀ of Mel frequency spectrum coefficient of the speech signal with noisy was extracted, and the product of MFCC₀ and spectral entropy was taken as fusion characteristic parameter of finally distinguishing speech segment from background noise. Then, the threshold value of MFPH characteristic parameters was estimated adaptively based on combination of fuzzy C-means clustering algorithm (FCM) and Bayesian information criterion (BIC). Finally, the double-threshold method was adopted for the voice activity detection. Experiments show that the accuracy of the proposed method is greatly improved in the -5~15 dB low SNR environment compared with traditional methods.