Deep Learning Based Speech Quality Prediction
Mittag, Gabriel.
Deep Learning Based Speech Quality Prediction [electronic resource] / by Gabriel Mittag. - 1st ed. 2022. - 1 online resource - T-Labs Series in Telecommunication Services, 2192-2829 . - T-Labs Series in Telecommunication Services, .
1. Introduction -- 2. Quality Assessment of Transmitted Speech -- 3. Neural Network Architectures for Speech Quality Prediction -- 4. Double-Ended Speech Quality Prediction Using Siamese Networks -- 5. Prediction of Speech Quality Dimensions With Multi-Task Learning -- 6. Bias-Aware Loss for Training From Multiple Datasets -- 7. NISQA – A Single-Ended Speech Quality Model -- 8. Conclusions -- A. Dataset Condition Tables -- B. Train and Validation Dataset Dimension Histograms -- References.
This book presents how to apply recent machine learning (deep learning) methods for the task of speech quality prediction. The author shows how recent 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 provides additional information about the cause of a quality impairment through the prediction of the speech quality dimensions of noisiness, coloration, discontinuity, and loudness.
9783030914790
10.1007/978-3-030-91479-0 doi
Signal processing.
User interfaces (Computer systems).
Human-computer interaction.
Natural language processing (Computer science).
Acoustical engineering.
Digital and Analog Signal Processing.
User Interfaces and Human Computer Interaction.
Natural Language Processing (NLP).
Engineering Acoustics.
Deep learning (Machine learning) Speech processing systems
Q325.73
Deep Learning Based Speech Quality Prediction [electronic resource] / by Gabriel Mittag. - 1st ed. 2022. - 1 online resource - T-Labs Series in Telecommunication Services, 2192-2829 . - T-Labs Series in Telecommunication Services, .
1. Introduction -- 2. Quality Assessment of Transmitted Speech -- 3. Neural Network Architectures for Speech Quality Prediction -- 4. Double-Ended Speech Quality Prediction Using Siamese Networks -- 5. Prediction of Speech Quality Dimensions With Multi-Task Learning -- 6. Bias-Aware Loss for Training From Multiple Datasets -- 7. NISQA – A Single-Ended Speech Quality Model -- 8. Conclusions -- A. Dataset Condition Tables -- B. Train and Validation Dataset Dimension Histograms -- References.
This book presents how to apply recent machine learning (deep learning) methods for the task of speech quality prediction. The author shows how recent 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 provides additional information about the cause of a quality impairment through the prediction of the speech quality dimensions of noisiness, coloration, discontinuity, and loudness.
9783030914790
10.1007/978-3-030-91479-0 doi
Signal processing.
User interfaces (Computer systems).
Human-computer interaction.
Natural language processing (Computer science).
Acoustical engineering.
Digital and Analog Signal Processing.
User Interfaces and Human Computer Interaction.
Natural Language Processing (NLP).
Engineering Acoustics.
Deep learning (Machine learning) Speech processing systems
Q325.73
