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.

What Is Speaker Recognition The identification of a person based on the features of their voice is referred to as "speaker recognition." The purpose of this information is to provide an answer to the query "Who is speaking?" Speech recognition and speaker recognition are both included in the broader concept of voice recognition. Verification of a speaker is distinct from identification of a speaker, and recognition of a speaker is not the same as diarization of a speaker. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Speaker recognition Chapter 2: Speech recognition Chapter 3: Voice analysis Chapter 4: Authentication Chapter 5: Interactive voice response Chapter 6: Biometrics Chapter 7: Electronic authentication Chapter 8: Multi-factor authentication Chapter 9: BioAPI Chapter 10: PerSay (II) Answering the public top questions about speaker recognition. (III) Real world examples for the usage of speaker recognition in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of speaker recognition' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of speaker recognition.

This volume and its companion volume LNAI 4441 constitute a state-of-the-art survey in the field of speaker classification. Together they address such intriguing issues as how speaker characteristics are manifested in voice and speaking behavior. The nineteen contributions in this volume are organized into topical sections covering fundamentals, characteristics, applications, methods, and evaluation.

This book discusses large margin and kernel methods for speech and speaker recognition Speech and Speaker Recognition: Large Margin and Kernel Methods is a collation of research in the recent advances in large margin and kernel methods, as applied to the field of speech and speaker recognition. It presents theoretical and practical foundations of these methods, from support vector machines to large margin methods for structured learning. It also provides examples of large margin based acoustic modelling for continuous speech recognizers, where the grounds for practical large margin sequence learning are set. Large margin methods for discriminative language modelling and text independent speaker verification are also addressed in this book. Key Features: Provides an up-to-date snapshot of the current state of research in this field Covers important aspects of extending the binary support vector machine to speech and speaker recognition applications Discusses large margin and kernel method algorithms for sequence prediction required for acoustic modeling Reviews past and present work on discriminative training of language models, and describes different large margin algorithms for the application of part-of-speech tagging Surveys recent work on the use of kernel approaches to text-independent speaker verification, and introduces the main concepts and algorithms Surveys recent work on kernel approaches to learning a similarity matrix from data This book will be of interest to researchers, practitioners, engineers, and scientists in speech processing and machine learning fields.

The production of truly natural-sounding speech still poses considerable problems, and the reliable recognition of continuous speech is still open to major improvements. This text captures the essential elements of current research on artificial speech synthesis and recognition.

With an A–Z format, this encyclopedia provides easy access to relevant information on all aspects of biometrics. It features approximately 250 overview entries and 800 definitional entries. Each entry includes a definition, key words, list of synonyms, list of related entries, illustration(s), applications, and a bibliography. Most entries include useful literature references providing the reader with a portal to more detailed information.

This book on Robust Speech Recognition and Understanding brings together many different aspects of the current research on automatic speech recognition and language understanding. The first four chapters address the task of voice activity detection which is considered an important issue for all speech recognition systems. The next chapters give several extensions to state-of-the-art HMM methods. Furthermore, a number of chapters particularly address the task of robust ASR under noisy conditions. Two chapters on the automatic recognition of a speaker's emotional state highlight the importance of natural speech understanding and interpretation in voice-driven systems. The last chapters of the book address the application of conversational systems on robots, as well as the autonomous acquisition of vocalization skills.

The author covers the fundamentals of both information and communication security including current developments in some of the most critical areas of automatic speech recognition. Included are topics on speech watermarking, speech encryption, steganography, multilevel security systems comprising speaker identification, real transmission of watermarked or encrypted speech signals, and more. The book is especially useful for information security specialist, government security analysts, speech development professionals, and for individuals involved in the study and research of speech recognition at advanced levels.