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Gabriel Mittag

Deep Learning Based Speech Quality Prediction

Deep Learning Based Speech Quality Prediction

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  • More about Deep Learning Based Speech Quality Prediction


This book provides an in-depth analysis of the suitability of different deep learning architectures for the task of speech quality prediction and demonstrates how the resulting model outperforms traditional speech quality models.

Format: Paperback / softback
Length: 165 pages
Publication date: 26 February 2023
Publisher: Springer Nature Switzerland AG


This book is a comprehensive guide that delves into the realm of applying cutting-edge machine learning (deep learning) techniques to the challenging task of speech quality prediction. The author takes readers on a journey of exploring how recent advancements in this field can be harnessed to enhance the accuracy and reliability of speech quality assessment. With a deep understanding of the suitability of various deep learning architectures for this specific task, the author provides an in-depth analysis of their capabilities and limitations.

Furthermore, the author showcases the remarkable performance of the resulting model, surpassing traditional speech quality models in terms of accuracy. By predicting the speech quality dimensions of noisiness, coloring, discontinuity, and loudness, the book goes beyond mere prediction and offers valuable insights into the underlying causes of quality impairments.

This book is a valuable resource for researchers, engineers, and practitioners in the field of speech processing and communication, as it offers a practical and insightful approach to leveraging deep learning for speech quality prediction. It provides a solid foundation for those seeking to advance their knowledge and expertise in this area.

In summary, this book is a must-read for anyone interested in exploring the potential of machine learning and deep learning in the realm of speech quality prediction. It offers a comprehensive and practical guide that empowers readers to unlock the full potential of these technologies and make meaningful contributions to the field.

Weight: 285g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030914813
Edition number: 1st ed. 2022

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