Novel deep autoencoder features for non-intrusive speech quality assessment

摘要

To emulate the human perception in quality assessment, an objective metric or assessment method is required, which is a challenging task. Moreover, assessing the quality of speech without any reference or the ground truth is altogether more difficult. In this paper, we propose a new non-intrusive speech quality assessment metric for objective evaluation of speech quality. The originality of proposed scheme lies in using deep autoencoder to extract low-dimensional features from a spectrum of the speech signal and finds a mapping between features and subjective scores using an artificial neural network (ANN). We have shown that autoencoder features capture noise information in a better way than state-of-the-art Filterbank Energies (FBEs). Quantification of our experimental results suggests that proposed metric gives more accurate and correlated scores than an existing benchmark for objective, non-intrusive quality assessment metric ITU-T P.563 standard.