Machine Learning for Speaker Recognition

This book provides a comprehensive toolkit for developing robust speaker recognition systems using machine learning and deep learning techniques, covering fundamental and advanced models, probabilistic approaches, and practical applications. It is ideal for researchers, practitioners, and engineers in the field.

\n Format: Hardback
\n Length: 334 pages
\n Publication date: 19 November 2020
\n Publisher: Cambridge University Press
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This comprehensive book is designed to assist readers in gaining a deep understanding of fundamental and advanced statistical models, as well as deep learning models, for robust speaker recognition and domain adaptation. By providing a valuable toolkit, readers will be empowered to apply machine learning techniques to address practical challenges such as robustness under adverse acoustic environments and domain mismatch when deploying speaker recognition systems.

The book offers a comprehensive exploration of state-of-the-art machine learning techniques for speaker recognition, encompassing a diverse range of probabilistic models, learning algorithms, case studies, and emerging trends and directions in speaker recognition based on modern machine learning and deep learning. It serves as an invaluable resource for graduates, researchers, practitioners, and engineers in the fields of electrical engineering, computer science, and applied mathematics.

In the first chapter, the book provides an introduction to speaker recognition, covering its historical development, challenges, and applications. It then delves into the fundamentals of statistical models, including Gaussian Mixture Models (GMMs), Hidden Markov Models (HMMs), and Support Vector Machines (SVMs). The chapter also discusses the importance of feature extraction and the use of temporal and spectral features in speaker recognition.

The second chapter focuses on deep learning models for speaker recognition, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks. The chapter explores the advantages and disadvantages of each model and discusses their implementation in speaker recognition systems. It also covers the use of transfer learning and data augmentation techniques to improve the performance of deep learning models.

In the third chapter, the book presents a range of probabilistic models for speaker recognition, including Gaussian Mixture Models (GMMs), Hidden Markov Models (HMMs), and Conditional Random Fields (CRFs). The chapter discusses the parameter estimation techniques used in these models and the challenges associated with their implementation. It also provides examples of real-world applications of probabilistic models in speaker recognition.

The fourth chapter explores learning algorithms for speaker recognition, including supervised learning, semi-supervised learning, and unsupervised learning. The chapter discusses the advantages and disadvantages of each learning algorithm and provides examples of their application in speaker recognition systems. It also covers the use of ensemble learning techniques to improve the performance of speaker recognition systems.

The fifth chapter presents case studies and practical examples of speaker recognition systems, including voice-activated assistants, call centers, and security systems. The chapter discusses the design and implementation of these systems and highlights the challenges and limitations associated with their deployment. It also provides insights into the future trends and directions in speaker recognition.

In the concluding chapter, the book summarizes the key findings and insights presented throughout the book. It provides recommendations for future research and development in the field of speaker recognition and highlights the potential applications of speaker recognition in various industries.

Overall, this comprehensive book offers a valuable resource for anyone interested in gaining a deep understanding of speaker recognition and domain adaptation. It provides a comprehensive exploration of state-of-the-art machine learning techniques, probabilistic models, learning algorithms, case studies, and emerging trends and directions. With its practical insights and applications, the book is suitable for graduates, researchers, practitioners, and engineers in electrical engineering, computer science, and applied mathematics.

\n Weight: 756g\n
Dimension: 176 x 250 x 21 (mm)\n
ISBN-13: 9781108428125\n \n