Titlebook: Deep Learning Based Speech Quality Prediction; Gabriel Mittag Book 2022 The Editor(s) (if applicable) and The Author(s), under exclusive l

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Book 2022 advancements in machine learning can be leveraged for the task of speech quality prediction and provides an in-depth analysis of the suitability of different deep learning architectures for this task. The author then shows how the resulting model outperforms traditional speech quality models and pr

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Prediction of Speech Quality Dimensions with Multi-Task Learning,elp to understand the underlying reason for a low MOS score. The subjective ground truth values of these scores are perceptual speech quality dimensions. Because the model aims to predict the overall MOS and additionally dimension scores from the same speech signal, the prediction can be seen as a Multi-Task-Learning (MTL) problem.

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3D IC Cooling with Micro-Fluidic Channelson for the time-alignment problem that occurs for speech signals transmitted through VoIP networks and shows how the clean reference signal can be incorporated into speech quality models that are based on end-to-end trained neural networks.

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