Non-Intrusive Objective Speech Quality Measurement Based on Fuzzy GMM and SVR for Narrowband Speech

Based on fuzzy Gaussian mixture model (FGMM) and support vector regression (SVR), an improved version of non-intrusive objective measurement for assessing quality of output speech without inputting clean speech is proposed for narrowband speech. Its perceptual linear predictive (PLP) features extracted from clean speech and clustered by FGMM are used as an artificial reference model. Input speech is separated into three classes, for each a consistency parameter between each feature pair from test speech signals and its counterpart in the pre-trained FGMM reference model is calculated and mapped to an objective speech quality score using SVR method. The correlation degree between subjective mean opinion score (MOS) and objective MOS is analyzed. Experimental results show that the proposed method offers an effective technique and can give better performances than the ITU-T P.563 method under most of the test conditions for narrowband speech.