NSQM: A non-intrusive assessment of speech quality using normalized energies of the neurogram

This study proposes a new non-intrusive measure of speech quality, the neurogram speech quality measure (NSQM), based on the responses of a biologically-inspired computational model of the auditory system for listeners with normal hearing. The model simulates the responses of an auditory-nerve fiber with a characteristic frequency to a speech signal, and the population response of the model is represented by a neurogram (2D time-frequency representation). The responses of each characteristic frequency in the neurogram were decomposed into sub-bands using 1D discrete Wavelet transform. The normalized energy corresponding to each sub-band was used as an input to a support vector regression model to predict the quality score of the processed speech. The performance of the proposed non-intrusive measure was compared to the results from a range of intrusive and non-intrusive measures using three standard databases: the EXP1 and EXP3 of supplement 23 to the P series (P.Supp23) of ITU-T Recommendations and the NOIZEUS databases. The proposed NSQM achieved an overall better result over most of the existing metrics for the effects of compression codecs, additive and channel noises. (C) Crown Copyright 2019 Published by Elsevier Ltd. All rights reserved.