Improved Speech-Presence Uncertainty Estimation Based on Spectral Gradient for Global Soft Decision-Based Speech Enhancement

摘要

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.