Fundamentals of Speaker Recognition

Speaker Recognition is a popular new technology that can authenticate voices over the phone. This comprehensive text covers the basics and the latest developments of this technology and includes examples and exercises at the end of each chapter.

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.

First comprehensive textbook to cover the latest developments in Speaker Recognition as well as fundamentals Includes many applications ranging from person identification or verification to multimedia applications Based on course material/tutorials of the author, a distinguished Adjunct Professor at Columbia University in New York, School of Engineering and Applied Science, and President and CEO of Recognition Technologies, Inc Includes supplementary material: sn.pub/extras

Inhalt

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.