Spectral subtraction with full-wave rectification and likelihood controlled instantaneous noise estimation for robust speech recognition
Haitian Xu, Zheng‐Hua Tan, Paul Dalsgaard, Børge Lindberg · 2004
In standard Spectral Subtraction (SS), Half-Wave Rectification SS (HWR-SS) is normally applied to avoid negative values in the Power Spectral Density (PSD) that occur mainly due to inaccurate noise estimation caused by a Voice Activity Detector (VAD). In this paper analyses show that, given accurate noise estimation, the phase relationship between speech and noise becomes the dominant cause of the negative values. FullWave Rectification based SS (FWR-SS) combined with Instantaneous Noise Estimation (INE) is therefore proposed to be applied instead of VAD based HWR-SS as it is better capable of maintaining the speech information in those negative values. It is also shown in the paper that FWR-SS provides optimum orthogonality between the estimated noise and speech signals. The INE method proposed in this paper is Likelihood Controlled Instantaneous Noise Estimation (LCINE), which combines long-term statistical characteristics of noise resulting from a VAD with a method of short-term INE. The combination of FWR-SS and LCINE is computationally efficient and shows a 51% error rate reduction on the Aurora 2 database in comparison to the basic Aurora front-end provided by ETSI [1].