Objective Assessment of Artificial Speech Bandwidth Extension Approaches

Inhalt:
Demonstrated in various prior investigations, wideband PESQ and POLQA are not capable to predict speech quality of artificial speech bandwidth extension (ABE) test conditions accurately enough. Based on a multilingual subjective listening test, evaluating ABE solutions from six different institutions, we present a novel objective measure particularly being able to estimate the speech quality of ABE-processed speech signals. The proposed measure employs a variety of features for ABE-related distortion detection. A combination of perceptually and non-perceptually influenced features are used as input to a support-vector-machine-based high-performance statistical model for establishing a link to an accurate speech quality estimate. Evaluation of the measure employs the ITU-Trecommended metrics correlation and rank order. Testing the predictive power of the proposed measure is done in three crossvaldation setups: Disjoint languages, disjoint speakers, and disjoint ABE solutions. In a comparison, the proposed measure outperforms wideband PESQ and POLQA both in correlation and rank order.