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Books > Computing & IT > Applications of computing > Artificial intelligence > Natural language & machine translation

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Machine Learning for Speaker Recognition (Hardcover) Loot Price: R2,812
Discovery Miles 28 120
Machine Learning for Speaker Recognition (Hardcover): Man-Wai Mak, Jen-Tzung Chien

Machine Learning for Speaker Recognition (Hardcover)

Man-Wai Mak, Jen-Tzung Chien

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Loot Price R2,812 Discovery Miles 28 120 | Repayment Terms: R264 pm x 12*

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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.

General

Imprint: Cambridge UniversityPress
Country of origin: United Kingdom
Release date: November 2020
Authors: Man-Wai Mak • Jen-Tzung Chien
Dimensions: 250 x 177 x 19mm (L x W x T)
Format: Hardcover
Pages: 334
ISBN-13: 978-1-108-42812-5
Categories: Books > Professional & Technical > Electronics & communications engineering > General
Books > Science & Mathematics > Mathematics > Applied mathematics > General
Books > Computing & IT > Computer software packages > Other software packages > Mathematical & statistical software
Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
Books > Computing & IT > Applications of computing > Artificial intelligence > Natural language & machine translation
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LSN: 1-108-42812-6
Barcode: 9781108428125

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