Feature analysis for the reverberation perception in speech signals

This paper considers the problem of quantifying the reverberation perception on speech signals. We investigate several combinations of three distinct reverberation-related features (namely, the reverberation time (RT), room spectral variance (RSV), and direct-to-reverberant energy ratio (DRR)), which can be extracted directly from the associated room impulse response. Particular attention is also paid on different post-processing nonlinear mappings in order to provide a more effective quality evaluation algorithm. Results indicate that the RSV feature can be completely disregarded if the RT and DRR estimates are properly weighted. Performance of resulting measure is slightly superior in comparison to previous state-of-the-art method, particularly with respect to the computational cost and robustness (by disregarding the RSV estimation), but also on the statistical correlation level with subjective grades of a large dataset of reverberant speech.