Over-Attenuated Components Regeneration for Speech Enhancement

Abstract

Despite the quality improvement of the speech signal with most traditional noise reduction (TNR) algorithms, the output is always distorted to some extent due to the over-attenuation of speech components. Weak speech components are usually regarded as noise in noise reduction processing and are therefore highly suppressed. In this paper, we propose a postprocessing technique which is based on the regeneration of both the voiced and unvoiced speech in the entire frequency domain to reduce this problem. A nonlinear transform is first applied to obtain the excitation signal, and a smooth envelope is then estimated. To utilize the information of the clean speech contained in the envelope, we combine the original TNR filter output with a weighted product of the excitation signal and the estimated envelope to generate the final synthesized speech. The synthesized speech is quite close to the clean speech and is more natural-sounding. Moreover, our algorithm can mask the residual musical noise effectively with the regenerated speech components. Experimental results demonstrate the excellent performance of our algorithm. In addition, we introduce two novel objective measures and further show the efficiency of our algorithm in maintaining the clean speech while reducing the noise as much as possible.