Speech enhancement based on soft‐masking exploiting both output SNR and selectivity of spatial filtering

Abstract

A speech enhancement method is presented, which applies a soft mask to a target speech output of spatial filtering, such as conventional beamforming or independent component analysis (ICA). In contrast to conventional methods using either outputs or filters estimated by spatial filtering, the mask is constructed by exploiting both local output signal-to-noise ratio (SNR) and spatial selectivity obtained from the directivity pattern of the estimated filters. Experiments were conducted for both ICA and minimum power distortionless response beamforming as spatial filtering in order to demonstrate that the described mask estimation is not a tuned method for particular preprocessing. The results in terms of both SNR with a retained speech ratio and word accuracy in speech recognition show that the described method can effectively suppress residual noise in the target speech output of spatial filtering.