Title: Speech endpoint detection method based on logarithmic energy entropy product of adaptive sub-bands in low signal-to-noise ratio environments

Authors: MingHui Zhu; Peng-Cheng Huang; JiaYong Zhang

Addresses: Jianghuai College, Anhui University, Hefei, 230031, China ' High Magnetic Field Laboratory, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China ' Sun Create Electronics Co., Ltd., Hefei, 230031, China

Abstract: In this paper, a detection method based on logarithmic energy entropy product of adaptive sub-bands is designed. After the speech signal is divided into frames and FFT, the probability of the existence of the speech signal is analysed according to the ratio of the minimum value of the local energy spectrum to the short-term energy spectrum. After the noise is suppressed according to the normal distribution of Gaussian noise, the logarithmic energy entropy product of adaptive sub-bands is calculated. Using the calculated results as a threshold, compare the logarithmic energy spectral ratio of the current speech frame with the threshold, and use Bayesian classification to detect speech endpoints. Experiment shows that the detection accuracy of this method is always higher than 94.4%, and the accuracy variance is between 0.055 and 0.072, effectively achieving the design expectations.

Keywords: voice signal; signal-to-noise ratio; SNR; voice endpoint; short time energy spectrum value; denoising; sub-bands logarithmic energy entropy product; accuracy.

DOI: 10.1504/IJBM.2024.138226

International Journal of Biometrics, 2024 Vol.16 No.3/4, pp.272 - 286

Received: 23 May 2023
Accepted: 17 Aug 2023
Published online: 30 Apr 2024 *

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