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: Hardback
Length: 165 pages
Publication date: 25 February 2022
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 an in-depth analysis of the suitability of various deep learning architectures for this specific task, the author demonstrates how these models can outperform traditional speech quality models.

Furthermore, the book goes beyond mere prediction and offers valuable insights into the underlying causes of quality impairments by predicting the speech quality dimensions of noisiness, coloring, discontinuity, and loudness. By unraveling these dimensions, the author provides valuable information for developers and researchers working in the field of speech processing and communication technologies.

The book is organized into well-structured chapters, each dedicated to exploring different aspects of speech quality prediction. The author begins by providing a foundational understanding of deep learning and its applications in speech processing. This includes a detailed explanation of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and other deep learning models, as well as their advantages and limitations.

The author then proceeds to showcase the effectiveness of deep learning in speech quality prediction. Through a series of case studies and practical examples, the author demonstrates how deep learning algorithms can be trained to accurately classify and predict speech quality parameters such as signal-to-noise ratio (SNR), speech intelligibility, and speech recognition accuracy. The book also discusses the challenges associated with training deep learning models and provides solutions to address them, such as data augmentation, regularization techniques, and optimization algorithms.

In addition to its technical aspects, the book also emphasizes the importance of ethical considerations in the development and deployment of speech quality prediction systems. The author discusses the potential risks and biases associated with machine learning algorithms and provides guidelines for ensuring fairness and transparency in the evaluation of speech quality.

Overall, this book is a valuable resource for researchers, developers, and practitioners in the field of speech processing and communication technologies. It provides a comprehensive and up-to-date overview of the latest machine learning methods for speech quality prediction, and offers practical insights and solutions for enhancing the accuracy and reliability of speech quality assessment. Whether you are a seasoned expert or just starting in this field, this book will help you gain a deeper understanding of the potential of deep learning in speech processing and its applications in real-world scenarios.

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 an in-depth analysis of the suitability of various deep learning architectures for this specific task, the author demonstrates how these models can outperform traditional speech quality models.

Furthermore, the book goes beyond mere prediction and offers valuable insights into the underlying causes of quality impairments by predicting the speech quality dimensions of noisiness, coloring, discontinuity, and loudness.

By unraveling these dimensions, the author provides valuable information for developers and researchers working in the field of speech processing and communication technologies.

The book is organized into well-structured chapters, each dedicated to exploring different aspects of speech quality prediction.

The author begins by providing a foundational understanding of deep learning and its applications in speech processing.

This includes a detailed explanation of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and other deep learning models, as well as their advantages and limitations.

The author then proceeds to showcase the effectiveness of deep learning in speech quality prediction.

Through a series of case studies and practical examples, the author demonstrates how deep learning algorithms can be trained to accurately classify and predict speech quality parameters such as signal-to-noise ratio (SNR), speech intelligibility, and speech recognition accuracy.

The book also discusses the challenges associated with training deep learning models and provides solutions to address them, such as data augmentation, regularization techniques, and optimization algorithms.

In addition to its technical aspects, the book also emphasizes the importance of ethical considerations in the development and deployment of speech quality prediction systems.

The author discusses the potential risks and biases associated with machine learning algorithms and provides guidelines for ensuring fairness and transparency in the evaluation of speech quality.

Overall, this book is a valuable resource for researchers, developers, and practitioners in the field of speech processing and communication technologies.

It provides a comprehensive and up-to-date overview of the latest machine learning methods for speech quality prediction, and offers practical insights and solutions for enhancing the accuracy and reliability of speech quality assessment.

Whether you are a seasoned expert or just starting in this field, this book will help you gain a deeper understanding of the potential of deep learning in speech processing and its applications in real-world scenarios.

Weight: 442g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030914783
Edition number: 1st ed. 2022