Perceptually motivated output-based speech quality assessment using neural networks
Dorel Picovici · 2004
A new output-based method for the prediction of subjective speech quality is proposed and its performance evaluated. The method is based on measuring perceptually motivated objective auditory distances between the voiced parts of the speech signal whose quality is to be evaluated to appropriately matching reference vectors extracted from a preformulated codebook. The codebook is formed by optimally clustering a large number of perceptually-based parametric vectors extracted from a database of clean speech signals. The auditory distance measures are then mapped into equivalent subjective scores, represented by the mean opinion scores (MOS), using regression. The required clustering and matching processes are achieved by using an efficient neural network based data mining technique known as the self-organising map. Perceptual speaker-independent parametric representation of the speech is achieved by using a linear prediction (PLP) model and bark spectrum analysis. The reported evaluation results show that the proposed system is robust against speaker, utterance and distortion variations.