Speech Signal Processing
|Title||Improved A Posteriori Speech Presence Probability Estimation Based on a Likelihood Ratio with Fixed Priors|
|Authors||Timo Gerkmann, Colin Breithaupt, Rainer Martin|
|Journal||Trans. Audio, Speech and Language Processing|
|Vol. ||16 |
|No. ||5 |
|pp ||910-919 |
In this contribution we present an improved estimator for the speech presence probability at each time-frequency point in the short-time Fourier-transform domain. In contrast to existing approaches this estimator does not rely on an adaptively estimated and thus signal dependent a priori signal-to-noise ratio estimate. It therefore decouples the estimation of the speech presence probability from the estimation of the clean speech spectral coefficients in a speech enhancement task. Using both a fixed a priori signal-to-noise ratio and a fixed prior probability of speech presence, the proposed a posteriori speech presence probability estimator achieves probabilities close to zero for speech absence and probabilities close to one for speech presence. While state-of-the-art speech presence probability estimators use adaptive prior probabilities and signal-to-noise ratio estimates we argue that these quantities should reflect true a priori information that shall not depend on the observed signal. We present a detection theoretic framework for determining the fixed a priori signal-to-noise ratio. The proposed estimator is conceptually simple and yields a better trade-off between speech distortion and noise leakage than state-of-the-art estimators.
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