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Speech quality, as perceived by humans, is an important performance metric for telephony and voice services. It is typically measured through subjective listening tests, which can be tedious and expensive. Algorithms such as PESQ and POLQA serve as a computational proxy for subjective listening tests. Here we propose using a convolutional neural network to predict the perceived quality of speech with noise, reverberation, and distortions, both intrusively and non-intrusively, i.e., with anddoi:10.1109/waspaa.2019.8937202 dblp:conf/waspaa/GamperRCTG19 fatcat:7dkhonkgtzap3mudewhm5kw6me