Spectrum-entropy improvement based speech endpoint detection method in low signal-to-noise ratio environment

Abstract

The invention provides a spectrum-entropy improvement based speech endpoint detection method in a low signal-to-noise ratio environment. In order to solve the problem that a speech endpoint detection system is not high in accuracy rate in the low signal-to-noise ratio environment in recognition of a current speaker, the endpoint detection method capable of improving accuracy rate of speech endpoint detection in the low signal-to-noise ratio environment is provided, wherein the detection method comprises the following steps: (1) pre-processing a speech signal in accordance with characteristics of the signal; (2) in accordance with division of each frame of frequency band of the speech signal, calculating spectrum-entropy and energy of various sub-bands, so that an energy-entropy ratio SEH of the various sub-bands is finally obtained; and (3) setting a proper threshold value, and in combination with median filtering, obtaining starting and ending positions of speech. The invention aims at removing influence of environmental noise by conducting the median filtering, so that the speech signal is more stable, and the accuracy rate of endpoint detection in the low signal-to-noise ratio environment is improved.

Description

technical field [0001] The invention relates to the field of speech signal processing, and proposes a speech endpoint detection method based on spectral entropy improvement. Background technique [0002] Speech endpoint detection is an important step in speaker recognition, and effective endpoint detection is the first problem to be solved in speaker recognition processing. Traditional endpoint detection algorithms, such as using zero-crossing rate, short-term energy and autocorrelation parameters, can obtain better detection results in high SNR environments, but their detection performance drops sharply in low SNR environments. Contents of the invention [0003] The present invention aims to solve the above problems of the prior art. An improved endpoint detection method based on spectral entropy with higher accuracy is proposed. Technical scheme of the present invention is as follows: [0004] A speech endpoint detection method based on spectral entropy improvement un...

AI Technical Summary

Problems solved by technology

Traditional endpoint detection algorithms, such as using zero-crossing rate, short-term energy and autocorrelation parameters, can obtain better detection results in high SNR environments, but their detection performance drops sharply in low SNR environments.

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