An Improved Method for Estimating the a Priori Probability of Speech Absence for Enhancement of Speech

摘要

The efficiency of many noise suppression filters relied on an accurate estimation of their parameters such as the power spectral density of the noise (PSD) and the a priori speech absence probability (SAP). This work addresses the problem of estimation of the a priori SAP. The proposed method relied on the conditional probabilities of the noisy speech magnitude, assuming that speech is absent or present and by dynamically computed parameters of the a priori SAP using two factors: a smoothing-update factor and a factor related to the kth spectral component. The smoothing-update factor, which is based on a decision made in frequency band whether speech is present or absent, is computed by recursively averaging past spectral values of the a priori SAP. The factor related to the kth spectral component is computed using the a priori signal-to-noise ratio (SNR). The efficiency of the proposed algorithm over competitive ones, both in terms of background noise suppression and speech distortion, is assessed by the use of an objective measure namely average segmental signal-to-noise ratio (segSNR) plus a study of speech spectrograms.