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A01=Jen-Tzung Chien
A01=Man-Wai Mak
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Author_Jen-Tzung Chien
Author_Man-Wai Mak
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Machine Learning for Speaker Recognition

English

By (author): Jen-Tzung Chien Man-Wai Mak

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. See more
Current price €96.89
Original price €101.99
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A01=Jen-Tzung ChienA01=Man-Wai MakAge Group_UncategorizedAuthor_Jen-Tzung ChienAuthor_Man-Wai Makautomatic-updateCategory1=Non-FictionCategory=PBWCategory=TJCategory=UFMCategory=UYQLCategory=UYQMCOP=United KingdomDelivery_Delivery within 10-20 working daysLanguage_EnglishPA=In stockPrice_€50 to €100PS=Activesoftlaunch
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Product Details
  • Weight: 760g
  • Dimensions: 177 x 250mm
  • Publication Date: 19 Nov 2020
  • Publisher: Cambridge University Press
  • Publication City/Country: United Kingdom
  • Language: English
  • ISBN13: 9781108428125

About Jen-Tzung ChienMan-Wai Mak

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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