Overview
- Presents 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 of different deep learning
Part of the book series: T-Labs Series in Telecommunication Services (TLABS)
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About this book
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.
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Keywords
Table of contents (8 chapters)
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Front Matter
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Back Matter
Authors and Affiliations
About the author
Gabriel Mittag received his B.Sc. and M.Sc. degree in electrical and electronic engineering at the Technische Universität Berlin. During his master's degree he spent two semesters at the RMIT University in Melbourne, Australia and focused primarily on biomedical and speech signal processing. From 2016 he was employed as research assistant at the Quality and Usability Lab at the TU Berlin, where he finished his PhD on the machine learning based prediction of speech quality. In May 2021, Gabriel Mittag started as Machine Learning Scientist at Microsoft in Redmond, WA, USA.
Bibliographic Information
Book Title: Deep Learning Based Speech Quality Prediction
Authors: Gabriel Mittag
Series Title: T-Labs Series in Telecommunication Services
DOI: https://doi.org/10.1007/978-3-030-91479-0
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
Hardcover ISBN: 978-3-030-91478-3Published: 25 February 2022
Softcover ISBN: 978-3-030-91481-3Published: 26 February 2023
eBook ISBN: 978-3-030-91479-0Published: 24 February 2022
Series ISSN: 2192-2810
Series E-ISSN: 2192-2829
Edition Number: 1
Number of Pages: XIV, 165
Number of Illustrations: 4 b/w illustrations, 54 illustrations in colour
Topics: Signal, Image and Speech Processing, User Interfaces and Human Computer Interaction, Natural Language Processing (NLP), Engineering Acoustics