Speech enhancement by combining statistical estimators of speech and noise

This paper presents a novel speech enhancement algorithm that can substantially improve the signal-to-residual spectrum ratio by combining statistical estimators of the spectral magnitude of the speech and noise. The noise spectral magnitude estimator is derived from the speech magnitude estimator, by appropriately transforming the a priori and the a posteriori SNR values. By expressing the signal-to-residual spectrum ratio as a function of the estimator's gain function, we derive a hybrid strategy that can improve the signal-to-residual spectrum ratio when the a priori and the a posteriori SNR are detected to be lower than 0 dB. Experimental results showed that the signal-to-residual spectrum ratio as well as the PESQ scores can be improved substantially in stationary and quasi-stationary noise conditions with the proposed hybrid estimators. Informal listening tests revealed improved speech quality and no musical noise.