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Machine learning for speaker recognition

Chien, Jen-Tzung.

Machine learning for speaker recognition

紀錄類型:	書目-語言資料,印刷品 : 單行本
作者:	MakM. W.,
其他作者:	ChienJen-Tzung.,
出版地:	Cambridge
出版者:	Cambridge University Press;
出版年:	2021.
面頁冊數:	xviii, 309 p.ill. : 26 cm.;
標題:	Automatic speech recognition. -
標題:	Biometric identification. -
標題:	Machine learning. -
摘要註:	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.
ISBN:	9781108428125

Machine learning for speaker recognition
Mak, M. W.

Machine learning for speaker recognition / Man-Wai Mak, Jen-Tzung Chien. - Cambridge : Cambridge University Press, 2021.. - xviii, 309 p. ; ill. ; 26 cm..
Includes bibliographical references (p. 289-306) and index..
ISBN 9781108428125
Automatic speech recognition.Biometric identification.Machine learning.

Chien, Jen-Tzung.

Machine learning for speaker recognition
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330 $a 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.
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