Learn fundamental and advanced machine learning techniques for robust speaker recognition and domain adaptation with this useful toolkit.
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Man-Wai Mak is Associate Professor of Department of Electronic and Information Engineering at The Hong Kong Polytechnic University.
Jen-Tzung Chien is a Chair Professor at the Department of Electrical and Computer Engineering, National Chiao Tung University, Taiwan. He has published extensively, including the book Bayesian Speech and Language Processing (Cambridge 2015). He is currently serving as an elected member of the IEEE Machine Learning for Signal Processing (MLSP) Technical Committee.
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Anbieter: Prior Books Ltd, Cheltenham, Vereinigtes Königreich
Hardcover. Zustand: Like New. First Edition. A firm, square hardback with strong joints, just showing a slight nick at the corner. Hence a non-text page is stamped 'damaged'. Despite such this book is very good condition. The contents are crisp, fresh and tight. And so it looks and feels unread and is now offered for sale at a very reasonable price. Artikel-Nr. 135634
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Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
Zustand: New. In English. Artikel-Nr. ria9781108428125_new
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Anbieter: Kennys Bookstore, Olney, MD, USA
Zustand: New. 2020. Hardcover. . . . . . Books ship from the US and Ireland. Artikel-Nr. V9781108428125
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Anbieter: moluna, Greven, Deutschland
Zustand: New. Understand fundamental and advanced statistical and deep learning models for robust speaker recognition and domain adaptation. Presenting state-of-the-art machine learning techniques for speaker recognition, this useful toolkit is perfect for graduates, res. Artikel-Nr. 352966759
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Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
Hardcover. Zustand: Brand New. 309 pages. 9.75x6.75x0.75 inches. In Stock. Artikel-Nr. x-1108428126
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Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch. Zustand: Neu. Neuware - 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. Artikel-Nr. 9781108428125
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