5.9.3 Models
Current objective methods such as Perceptual Evaluation of Speech Quality (PESQ) [466] and
Perceptual Objective Listening Quality Assessment (POLQA) [36] for evaluating the quality of
speech mostly rely on the availability of the corresponding clean reference. These methods fail
in real-world scenarios where the ground truth clean reference is unavailable. In recent years,
several attempts to automatically estimate the MOS using neural networks for performing quality
assessment and predicting ratings or scores have attracted much attention [55, 57, 118, 119, 404,
514]. These approaches outperform traditional approaches without the need for a clean reference.
However, they lack robustness and generalization capabilities, limiting their use in real-world
applications. The authors in [404] explore Deep machine listening for Estimating Speech Quality
(DESQ) for predicting the perceived speech quality based on phoneme posterior probabilities
AReviewofDeepLearningTechniquesforSpeechProcessing