摘要：In this paper, a single-ended quality measurement algorithm for noise suppressed speech is described.The proposed algorithm computes fast approximations of Kullback-Leibler distances between Gaussian mixture (GM) reference models of clean, noise corrupted, and noise suppressed speech and a GM model trained online on the test speech signal. The distances, together with a spectral flatness measure, are mapped to an estimated quality score via a support vector regressor. Experimental results show that substantial improvement in performance and complexity can be attained, relative to the current state-of-art single-ended ITU-T P.563 algorithm. Due to its modular architecture, the proposed algorithm can be easily configured to also perform signal distortion and background intrusiveness measurement, a functionality not available with current standard algorithms.
