The Research On Speech Enhancement Method And Its Application

The purpose of speech enhancement is extracting speeeh information from noisy speeeh signal with might and main, which is extensively used in speeeh reeognition, speeeh coding, speeeh communication and other fields.speech enhancement is becoming an important branch of speech signal processing now.This paper mainly studied the enhancement methods of speeeh signal which is polluted by additive noise . The main works and innovations are as follows:(1) The basic principles of several speech enhancement methods are discussed such as spectral subtraction method, Wiener filtering method,Kalman filtering method.The strongpoint and shortcoming of the above methods are also compared with each other in this paper.(2)The basic principle of wavelet transform is discussed,and the common wavelet function is introduced.The different characters of wavelet analysis coefficients between speech signal and noise signal are analysed,and the principle of wavelet denoising is expatiated.A new method with an improved threshold function is proposed in this paper. Experimental results show that a satisfying de-noising result can be obtained by this method compared with traditional methods.(3)The thoeries of wavelet packet transform and wavelet packet denoising are discussed,the experimnetal sumulations show the validity of this method compared with wavelet denoising,because wavelet packet ananlysis can decompose and process high-frequence signal,and a higher output SNR can be obtained.The thoeries of multi-wavelet transform and the common preprocessing methods are also introduced,which are usually used to denoise in image processing.We used the multi-wavelet thoery in speech denoising, the validity of this method can be proved by experimental sumulations.(4)The principles of speech enhancement by signal subspace are discussed,which are used in speech endpoint detection.A new speech endpoint detection method based on signal subspace and the information complexity is proposed,and an improved threshold is given to compute the information complexity to decide which is speech or not. Experimental simulations show that this method can increase the endpoint detection accuracy ratio compared with the traditional speech endpoint detection method, especially it has high endpoint detection accuracy ratio under low signal-to-noise ratio.