Machine Learning for Speaker Recognition
Autor Man-Wai Mak, Jen-Tzung Chienen Limba Engleză Hardback – 18 noi 2020
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Specificații
ISBN-13: 9781108428125
ISBN-10: 1108428126
Pagini: 334
Ilustrații: 133 b/w illus. 4 tables
Dimensiuni: 177 x 250 x 19 mm
Greutate: 0.77 kg
Editura: Cambridge University Press
Colecția Cambridge University Press
Locul publicării:Cambridge, United Kingdom
ISBN-10: 1108428126
Pagini: 334
Ilustrații: 133 b/w illus. 4 tables
Dimensiuni: 177 x 250 x 19 mm
Greutate: 0.77 kg
Editura: Cambridge University Press
Colecția Cambridge University Press
Locul publicării:Cambridge, United Kingdom
Cuprins
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.
Recenzii
'There is a need for an accessible textbook to help newcomers to enter the field [of automatic speaker recognition]. Machine Learning for Speaker Recognition by Man-Wai Mak and Jen-Tzung Chien serves such a need. Both authors are highly seasoned in the field. They cover both fundamental techniques and state-of-the-art methods at an accessible level using the language of modern probabilistic machine learning. The authors cover different components of speaker recognition systems including feature extraction, back-end modeling and scoring, along with various case studies. The book is well suited for the needs of graduate students and researchers in electrical engineering and computer science, along with practitioners. Apart from basic prerequisites in calculus, linear algebra, probabilities and statistics, the textbook provides a coherent and self-contained journey into what modern automatic speaker recognition is about.' Tomi Kinnunen, University of Eastern Finland
'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
'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
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Descriere
Learn fundamental and advanced machine learning techniques for robust speaker recognition and domain adaptation with this useful toolkit.