Machine Learning for Speaker Recognition

Learn fundamental and advanced machine learning techniques for robust speaker recognition and domain adaptation with this useful toolkit.

Man-Wai Mak (Author), Jen-Tzung Chien (Author)

9781108428125, Cambridge University Press

Hardback, published 19 November 2020

334 pages, 133 b/w illus. 4 tables
25 x 17.7 x 1.9 cm, 0.76 kg

'The topical coverage is spot-on, and the text discusses many key algorithms that support statistical learning approaches, including hybrid models, deep learning classification, and generative methods. In addition, the authors provide a deep mathematical exploration into versions of algorithms, optimization approaches, and domain adaptation statistics within the context of signal processing. The extensive diagrams, linear algebra notation, and mathematical calculus machinery will support developers who are building new implementations or need to look under the hood of existing systems. Highly Recommended.' J. Brzezinski, Choice

This book will help readers understand fundamental and advanced statistical models and deep learning models for robust speaker recognition and domain adaptation. This useful toolkit enables readers to apply machine learning techniques to address practical issues, such as robustness under adverse acoustic environments and domain mismatch, when deploying speaker recognition systems. Presenting state-of-the-art machine learning techniques for speaker recognition and featuring a range of probabilistic models, learning algorithms, case studies, and new trends and directions for speaker recognition based on modern machine learning and deep learning, this is the perfect resource for graduates, researchers, practitioners and engineers in electrical engineering, computer science and applied mathematics.

Part I. Fundamental Theories: 1. Introduction
2. Learning algorithms
3. Machine learning models
Part II. Advanced Studies: 4. Deep learning models
5. Robust speaker verification
6. Domain adaptation
7. Dimension reduction and data augmentation
8. Future direction
Index.

Subject Areas: Machine learning [UYQM], Natural language & machine translation [UYQL], Mathematical & statistical software [UFM], Electronics & communications engineering [TJ], Applied mathematics [PBW]