Estimation of speech absence uncertainty based on multiple linear regression analysis for speech enhancement

• We propose a regression-based method for estimating the speech absence uncertainty.
• The regression model was trained under the various noise and SNR conditions.
• The proposed method showed better performance than other algorithms.

We propose a novel approach to improve the performance of speech enhancement systems by using multiple linear regression to improve the technique of estimating the speech presence uncertainty. Conventional speech enhancement techniques use a fixed ratio Q of the a priori probability of speech presence and speech absence, or determine the value of Q simply by comparing one particular parameter against a threshold in deriving the speech absence probability (SAP) associated with the speech presence uncertainty. To further improve the performance of the SAP, we attempt to adaptively change Q according to a linear model consisting of the regression coefficients obtained by results from multiple linear regression analysis and two principal parameters: a priori SNR and the ratio between the local energy of the noisy speech and its derived minimum since these parameters correlate strongly with the value of Q. Distinct values of Q for each frequency in each frame are consequently assigned in time which leads to improved tracking performance of speech absence uncertainty and thus better performance of the proposed speech enhancement compared to conventional approaches. The superiority of the proposed approach is confirmed through extensive objective and subjective evaluations under various noise conditions.