TY  - BOOK
AU  - Mittag,Gabriel
ED  - SpringerLink (Online service)
TI  - Deep Learning Based Speech Quality Prediction
T2  - T-Labs Series in Telecommunication Services,
SN  - 9783030914790
AV  - Q325.73 
PY  - 2022///
CY  - Cham
PB  - Springer International Publishing, Imprint: Springer
KW  - Signal processing
KW  - User interfaces (Computer systems)
KW  - Human-computer interaction
KW  - Natural language processing (Computer science)
KW  - Acoustical engineering
KW  - Digital and Analog Signal Processing
KW  - User Interfaces and Human Computer Interaction
KW  - Natural Language Processing (NLP)
KW  - Engineering Acoustics
KW  - Deep learning (Machine learning)
KW  - Speech processing systems
N1  - 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
N2  - 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
UR  - https://doi.org/10.1007/978-3-030-91479-0
ER  -