Speaker Perception And Recognition An Integrative Framework For Computational Speech Processing

Scholars of early Christian literature acknowledge that oral traditions lie behind the New Testament gospels. While the concept of orality is widely accepted, it has not resulted in a corresponding effort to understand the reception of the gospels within their oral milieu. In this book, Kelly Iverson reconsiders the experiential context in which early Christian literature was received and interpreted. He argues that reading and performance are distinguishable media events, and, significantly, that they produce distinctive interpretive experiences for readers and audiences alike. Iverson marshals an array of methodological perspectives demonstrating how performance generates a unique experiential context that shapes and informs the interpretive process. Iverson's study explores the dynamic oral environment in which ancient audiences experienced the gospel stories. He shows why an understanding of oral performance has important implications for the study of the NT, as well as for several issues that are largely unquestioned by biblical scholars.

Speech processing addresses various scientific and technologicalareas. It includes speech analysis and variable rate coding, inorder to store or transmit speech. It also covers speech synthesis,especially from text, speech recognition, including speaker andlanguage identification, and spoken language understanding. This book covers the following topics: how to realize speechproduction and perception systems, how to synthesize and understandspeech using state-of-the-art methods in signal processing, patternrecognition, stochastic modelling computational linguistics andhuman factor studies.

Speech Processing, Recognition and Artificial Neural Networks contains papers from leading researchers and selected students, discussing the experiments, theories and perspectives of acoustic phonetics as well as the latest techniques in the field of spe ech science and technology. Topics covered in this book include; Fundamentals of Speech Analysis and Perceptron; Speech Processing; Stochastic Models for Speech; Auditory and Neural Network Models for Speech; Task-Oriented Applications of Automatic Speech Recognition and Synthesis.

In this text, the editors aim to convert the mapping of speech patterns into mental representations. They cover theories of perception and cognition, issues in clinical speech pathology, and the practical concerns of speech technology.

Research in the field of automatic speech and speaker recognition has made a number of significant advances in the last two decades, influenced by advances in signal processing, algorithms, architectures, and hardware. These advances include: the adoption of a statistical pattern recognition paradigm; the use of the hidden Markov modeling framework to characterize both the spectral and the temporal variations in the speech signal; the use of a large set of speech utterance examples from a large population of speakers to train the hidden Markov models of some fundamental speech units; the organization of speech and language knowledge sources into a structural finite state network; and the use of dynamic, programming based heuristic search methods to find the best word sequence in the lexical network corresponding to the spoken utterance. Automatic Speech and Speaker Recognition: Advanced Topics groups together in a single volume a number of important topics on speech and speaker recognition, topics which are of fundamental importance, but not yet covered in detail in existing textbooks. Although no explicit partition is given, the book is divided into five parts: Chapters 1-2 are devoted to technology overviews; Chapters 3-12 discuss acoustic modeling of fundamental speech units and lexical modeling of words and pronunciations; Chapters 13-15 address the issues related to flexibility and robustness; Chapter 16-18 concern the theoretical and practical issues of search; Chapters 19-20 give two examples of algorithm and implementational aspects for recognition system realization. Audience: A reference book for speech researchers and graduate students interested in pursuing potential research on the topic. May also be used as a text for advanced courses on the subject.

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.

Based on years of instruction and field expertise, this volume offers the necessary tools to understand all scientific, computational, and technological aspects of speech processing. The book emphasizes mathematical abstraction, the dynamics of the speech process, and the engineering optimization practices that promote effective problem solving in this area of research and covers many years of the authors' personal research on speech processing. Speech Processing helps build valuable analytical skills to help meet future challenges in scientific and technological advances in the field and considers the complex transition from human speech processing to computer speech processing.