PERCEPTUAL SUBSPACE SPEECH ENHANCEMENT WITH SSDR NORMALIZATION

In this paper a perceptual subspace speech enhancement method using masking property of human auditory system with signal to spectral deviation ratio (SSDR) normalization is presented. The masking property of the human auditory system is used while deciding the gain parameters for the algorithm. Spectral Domain Constrained estimator was employed in determining the filter coefficients and colored noise was handled by replacing the noise variance by Rayleigh quotient. SSDR normalization is further done to reduce the spectral distortion, making the output more intelligible. The objective measures SNRLOSS and wcep were chosen for performance evaluation based on their efficiency in determining the intelligibility of the output. The results show an improved performance of the proposed method over some of the existing speech enhancement methods in terms of intelligibility.