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Can lend chapters, not whole ebooks
Title
Deep learning based speech quality prediction/ Gabriel Mittag.
ISBN
9783030914790 (electronic bk.)
3030914798 (electronic bk.)
303091478X
9783030914783
Imprint
Cham, Switzerland : Springer, 2022.
Language
English
Description
1 online resource
Other Standard Identifiers
10.1007/978-3-030-91479-0 doi
Call Number
Q325.73
Dewey Decimal Classification
006.3/1
Summary
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.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed March 10, 2022).
Series
T-labs series in telecommunication services.
Available in Other Form
Print version: 9783030914783
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.
