Buch, Englisch, 942 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 1486 g
Buch, Englisch, 942 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 1486 g
ISBN: 978-1-4899-7922-3
Verlag: Springer US
An emerging technology, Speaker Recognition is becoming well-known for providing voice authentication over the telephone for helpdesks, call centres and other enterprise businesses for business process automation.
"Fundamentals of Speaker Recognition" introduces Speaker Identification, Speaker Verification, Speaker (Audio Event) Classification, Speaker Detection, Speaker Tracking and more. The technical problems are rigorously defined, and a complete picture is made of the relevance of the discussed algorithms and their usage in building a comprehensive Speaker Recognition System.
Designed as a textbook with examples and exercises at the end of each chapter, "Fundamentals of Speaker Recognition" is suitable for advanced-level students in computer science and engineering, concentrating on biometrics, speech recognition, pattern recognition, signal processing and, specifically, speaker recognition. It is also a valuable reference for developers of commercial technology and for speech scientists.
Please click on the link under "Additional Information" to view supplemental information including the Table of Contents and Index.
Zielgruppe
Professional/practitioner
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Spracherkennung, Sprachverarbeitung
- Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik
Weitere Infos & Material
Preface.- Basic Theory.- Introduction.- Speaker and Vocal Tract Modeling.- Signal Processing and Feature Extraction Techniques.- Data Representation and Probability Distributions.- Information Theory.- Metrics and Distortion Measures Bayesian Learning and Gaussian Mixture Modeling.- Parameter Estimation and Learning.- Hidden Markov Modeling (HMM).- Support Vector Machines.- Neural Networks.- Advanced Theory.- Speaker Modeling.- Algorithms.- Practice.- Speaker Recognition.- Overall Design.- Representation of Results.- Glossary.- Index.
