Speech enhancement method using voice existence probability

Abstract

The invention discloses a speech enhancement method using a speech existence probability, after the processing of the invention, the speech quality is higher, and the noise reduction amount is larger.The method is realized by the following technical scheme that: on the basis of a MIS measure speech enhancement method, the speech existence probability is used, an inputted noisy speech is sampled,framed and windowed, and then a noisy speech spectrum is obtained through fast Fourier transform (FFT); the noise estimation of the obtained speech spectrum is carried out, based on a non-stationary noise minimum search algorithm of statistical information, the smoothing between front and back frame noise estimation values is carried out by using inter-frame correlation, and a noise power spectrumis estimated; at the same time, speech prior signal-to-noise ratio estimated values obtained in several front and back frames are smoothened; then the speech existence probability and a MIS measure gain factor are combined, the obtained noisy speech spectrum is multiplied by a new gain factor, and a spectrum of the enhanced speech is obtained; and the inverse fast Fourier transform (IFFT) is performed to obtain an enhanced time domain speech signal.

Description

technical field [0001] The invention relates to a speech enhancement method in the field of wireless communication, in particular to a speech enhancement method using speech existence probability in a speech processing system. Background technique [0002] Language is one of the main media of human communication, and speech is its important form of expression. In daily social life, voice communication scenes can be seen everywhere, such as telephone communication, cochlear implants and human-computer interaction systems. Various noises will inevitably be mixed in the voice signal, resulting in a decrease in the intelligibility of the voice signal . Speech signals can generally be divided into silent segments, unvoiced segments, and voiced segments. The silent segment is the segment of background noise, with the lowest average energy; the voiced segment is the segment of the voice signal corresponding to the vibration of the vocal cords, with the highest average energy; the...

AI Technical Summary

Problems solved by technology

However, this method has insufficient suppression effect on the residual noise of the non-speech segment, and often there is still a large noise residue in the non-speech segment, which still has a certain impact on the human hearing experience.

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