In this paper, we propose a speech-presence uncertainty estimation to improve the global soft decision-based speech enhancement technique by using the spectral gradient scheme. The conventional soft decision-based speech enhancement technique uses a fixed ratio (Q) of the a priori speech-presence and speech-absence probabilities to derive the speech-absence probability (SAP). However, we attempt to adaptively change Q according to the spectral gradient between the current and past frames as well as the status of the voice activity in the previous two frames. As a result, the distinct values of Q to each frequency in each frame are assigned in order to improve the performance of the SAP by tracking the robust a priori information of the speech-presence in time.

In this paper, we propose a spectral difference approach for noise power estimation in speech enhancement. The noise power estimate is given by recursively averaging past spectral power values using a smoothing parameter based on the current observation. The smoothing parameter in time and frequency is adjusted by the spectral difference between consecutive frames that can efficiently characterize noise variation. Specifically, we propose an effective technique based on a sigmoid-type function in order to adaptively determine the smoothing parameter based on the spectral difference. Compared to a conventional method, the proposed noise estimate is computationally efficient and able to effectively follow noise changes under various noise conditions.