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.
"synopsis" may belong to another edition of this title.
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.
"About this title" may belong to another edition of this title.
Seller: Prior Books Ltd, Cheltenham, United Kingdom
Hardcover. Condition: 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. Seller Inventory # 135634
Quantity: 1 available
Seller: Books Puddle, New York, NY, U.S.A.
Condition: New. Seller Inventory # 26377281491
Seller: Majestic Books, Hounslow, United Kingdom
Condition: New. Seller Inventory # 369845260
Quantity: 1 available
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Condition: New. Seller Inventory # 40973292-n
Seller: Lucky's Textbooks, Dallas, TX, U.S.A.
Condition: New. Seller Inventory # ABLIING23Mar2317530282922
Seller: Biblios, Frankfurt am main, HESSE, Germany
Condition: New. Seller Inventory # 18377281497
Quantity: 1 available
Seller: California Books, Miami, FL, U.S.A.
Condition: New. Seller Inventory # I-9781108428125
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000. Seller Inventory # FM-9781108428125
Quantity: 9 available
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Condition: As New. Unread book in perfect condition. Seller Inventory # 40973292
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.
Hardcover. Condition: new. Hardcover. 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. 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, researchers, and engineers in electrical engineering, computer science and applied mathematics. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781108428125