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Prediction of Perceived Speech Quality Using Deep Machine Listening
2018
Interspeech 2018
Subjective ratings of speech quality (SQ) are essential for evaluating algorithms for speech transmission and enhancement. In this paper we explore a non-intrusive model for SQ prediction based on the output of a deep neural net (DNN) from a regular automatic speech recognizer. The degradation of phoneme probabilities obtained from the net is quantified with the mean temporal distance proposed earlier for multi-stream ASR. The SQ predicted with this method is compared with average subject
doi:10.21437/interspeech.2018-1374
dblp:conf/interspeech/OosterHM18
fatcat:bgl4yp6anjas3fwa7d2hni6om4