Improving Deep Models of Speech Quality Prediction through Voice Activity Detection and Entropy-based Measures

This paper explores Deep machine listening for Estimating Speech Quality (DESQ), which predicts the perceived speech quality based on phoneme posterior probabilities obtained from a deep neural network. The degradation of phonemes is quantified with the entropy-based Gini measure that is compared to the mean temporal distance (MTD) proposed earlier. Since long speech pauses might have a large effect on the speech quality, we investigate if a voice activity detection (VAD) has a beneficial or detrimental effect on the predictive power of our model. The evaluation is performed by correlating the model output and mean opinion scores (MOS) of normal-hearing listeners who rated signals degraded by typical VoIP artifacts. While the Gini-based measure and MTD result in very similar predictions (with a lower computational cost for the Gini-measure), the VAD increases performance from r = 0.87 to r = 0.91 which is higher than three competing baselines (ITU-P.563, ANIQUE+, and SRM-Rnorm).