An improved non-intrusive objective speech quality evaluation based on FGMM and FNN

An improved non-intrusive objective speech quality evaluation method is proposed based on Fuzzy Gaussian Mixture Model (FGMM) and Fuzzy Neural Network (FNN). The degraded speech is separated into three classes (unvoiced, voiced and silence), then for each class the consistency measurement between Perceptual Linear Predictive (PLP) features of the degraded speech and the pre-trained FGMM reference model is calculated and mapped to an objective speech quality score using FNN mapping method. The proposed method performs better than the previous work using GMM and ITU-T P.563 under the test conditions used in this paper.