Machine learning based non-intrusive quality estimation with an augmented feature set
Mona Hakami, Willem Bastiaan Kleijn · 2017
We present a method that improves the objective quality estimation of a speech utterance. We show that including raw features that are presumably redundant reduces the effect of input noise and improves the performance of linear regressors. To exploit this effect we propose the novel idea to augment the feature set with redundant features. The proposed augmented feature set and the neural network that consists of an auto-encoder and a linear regressor leads to improved prediction accuracy of the single-ended quality assessment approach. Evaluating the system on the ITU-T Supplement 23 database illustrates that the proposed approach outperforms the current state-of-the-art.