Abstract: 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.

Key words: voice activity detection, Mel frequency spectrum coefficient, spectral entropy, spectral entropy Mel product, double-threshold method, low signal-to-noise ratio