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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978-3-030-91481-3The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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Gabriel MittagPresents how to apply deep learning methods for the task of speech quality prediction.Includes a model that outperforms traditional speech quality models.Presents an in-depth analysis and comparison o

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Bias-Aware Loss for Training from Multiple Datasets,nted that considers the biases between different datasets by learning the biases automatically during training. The calculated loss is adjusted in a way that errors, which only occur due to dataset-specific biases, are not considered when optimising the weights of the quality prediction neural netwo

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Quality Assessment of Transmitted Speech,. Then the terms “speech quality” and “speech quality dimensions” are introduced, and subjective speech quality assessment methods are discussed. Afterwards, a review of instrumental speech quality prediction models is given. At first, traditional speech quality models are presented. Then machine le

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Neural Network Architectures for Speech Quality Prediction,ures are divided into three different stages. First, a frame-based neural network calculates features for each time step. The resulting feature sequence is then modelled by a time-dependency neural network. Finally, a pooling stage aggregates the sequence of features over time to estimate the overal