Machine learning for speaker recognition / Man-Wai Mak, Jen-Tzung Chien.
By: Mak, M. W [author.]
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Contributor(s): Chien, Jen-Tzung [author.].
Material type: 
BookPublisher: Cambridge : Cambridge University Press, 2020Description: 1 online resource (xviii, 309 pages) : digital, PDF file(s).Content type: text Media type: computer Carrier type: online resourceISBN: 9781108552332 (ebook).Subject(s): Automatic speech recognition | Biometric identification | Machine learningAdditional physical formats: Print version: : No titleDDC classification: 006.4/54 Online resources: Click here to access online Summary: 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.
Title from publisher's bibliographic system (viewed on 29 Jun 2020).
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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