A data-driven non-intrusive measure of speech quality and intelligibility

Abstract

Speech signals are often affected by additive noise and distortion which can degrade the perceived quality and intelligibility of the signal. We present a new measure, NISA, for estimating the quality and intelligibility of speech degraded by additive noise and distortions associated with telecommunications networks, based on a data driven framework of feature extraction and tree based regression. The new measure is non-intrusive, operating on the degraded signal alone without the need for a reference signal. This makes the measure applicable to practical speech processing applications operating in the single-ended mode. The new measure has been evaluated against the intrusive measures PESQ and STOI. The results indicate that the accuracy of the new non-intrusive method is around 90% of the accuracy of the intrusive measures, depending on the test scenario. The NISA measure therefore provides non-intrusive (single-ended) PESQ and STOI estimates with high accuracy.