Nonintrusive speech quality evaluation using an adaptive neurofuzzy inference system

Abstract

This letter presents a novel nonintrusive speech quality evaluation method using an adaptive neurofuzzy inference system (ANFIS). The proposed method employed a first-order Sugeno-type fuzzy inference system (FIS) to estimate speech quality using only the output signal of the system under test. This new method was compared with the state-of-the-art nonintrusive quality evaluation standard, the ITU-T P.563 Recommendation, using seven subjective quality databases of the ITU-T P-series Supplementary 23. Experimental results show that the correlation of the proposed method with the subjective quality scores reached 0.8812, with a standard error of 0.3647 across the entire database. This compares favorably with the standard P.563, which provides a correlation and standard error of 0.8422 and 0.4493, respectively.