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

淡紫色花

Double-Ended Speech Quality Prediction Using Siamese Networks,ded model of the previous “Neural Network Architectures” chapter but calculates a feature representation of the reference and the degraded signal through a Siamese CNN with Time-Dependency modelling network that shares the weights between both signals. The resulting features are then used to align t

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英寸

Bias-Aware Loss for Training from Multiple Datasets,nd truth MOS that are the target values of the supervised learning approach. In particular, it is common practice to use multiple datasets for training and validation, as subjective data is usually sparse due to the costs that experiments involve. However, these datasets often come from different la

密切關(guān)系

NISQA: A Single-Ended Speech Quality Model, previous chapters. Overall, the model is trained and evaluated on a wide variety of 78 different datasets. To train a model that delivers robust speech quality estimation for unknown speech samples, it is important to use speech samples that are highly diverse and come from different sources (i.e.

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Badger

Quality Assessment of Transmitted Speech,arning based models from literature, which are not based on deep learning, are described. Finally, a brief overview of deep learning architectures and deep learning based speech quality models is given.

Minutes

Neural Network Architectures for Speech Quality Prediction,l speech quality. It will be shown that the combination of a CNN for per-frame modelling, a self-attention network for time-dependency modelling, and an attention-pooling network for pooling yields the best overall performance.

精確

NISQA: A Single-Ended Speech Quality Model,data distributions). Because of this, in addition to newly created datasets for this work, also speech datasets from the POLQA pool, the ITU-T P Suppl. 23 pool, and further internal datasets are used. The model is then finally evaluated on a live-talking test dataset that contains recordings of real phone calls.

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