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.
['Dushyant Sharma', 'Yu Wang', 'Patrick A. Naylor', 'Mike Brookes']
A data-driven non-intrusive measure of speech quality and intelligibility