The term speech processing refers to the scientific discipline concerned with the analysis and processing of speech signals for getting the best benefit in various practical scenarios. These different practical scenarios correspond to a large variety of applications of speech processing research. Examples of some applications include enhancement, coding, synthesis, recognition and speaker recognition. A very rapid growth, particularly during the past ten years, has resulted due to the efforts of many leading scientists. The ideal aim is to develop algorithms for a certain task that maximize performance, are computationally feasible and are robust to a wide class of conditions. The purpose of this book is to provide a cohesive collection of articles that describe recent advances in various branches of speech processing. The main focus is in describing specific research directions through a detailed analysis and review of both the theoretical and practical settings. The intended audience includes graduate students who are embarking on speech research as well as the experienced researcher already working in the field. For graduate students taking a course, this book serves as a supplement to the course material. As the student focuses on a particular topic, the corresponding set of articles in this book will serve as an initiation through exposure to research issues and by providing an extensive reference list to commence a literature survey. Expe rienced researchers can utilize this book as a reference guide and can expand their horizons in this rather broad area.

This book focuses primarily on speech recognition and the related tasks such as speech enhancement and modeling. This book comprises 3 sections and thirteen chapters written by eminent researchers from USA, Brazil, Australia, Saudi Arabia, Japan, Ireland, Taiwan, Mexico, Slovakia and India. Section 1 on speech recognition consists of seven chapters. Sections 2 and 3 on speech enhancement and speech modeling have three chapters each respectively to supplement section 1. We sincerely believe that thorough reading of these thirteen chapters will provide comprehensive knowledge on modern speech recognition approaches to the readers.

Modern communication devices, such as mobile phones, teleconferencing systems, VoIP, etc., are often used in noisy and reverberant environments. Therefore, signals picked up by the microphones from telecommunication devices contain not only the desired near-end speech signal, but also interferences such as the background noise, far-end echoes produced by the loudspeaker, and reverberations of the desired source. These interferences degrade the fidelity and intelligibility of the near-end speech in human-to-human telecommunications and decrease the performance of human-to-machine interfaces (i.e., automatic speech recognition systems). The proposed book deals with the fundamental challenges of speech processing in modern communication, including speech enhancement, interference suppression, acoustic echo cancellation, relative transfer function identification, source localization, dereverberation, and beamforming in reverberant environments. Enhancement of speech signals is necessary whenever the source signal is corrupted by noise. In highly non-stationary noise environments, noise transients, and interferences may be extremely annoying. Acoustic echo cancellation is used to eliminate the acoustic coupling between the loudspeaker and the microphone of a communication device. Identification of the relative transfer function between sensors in response to a desired speech signal enables to derive a reference noise signal for suppressing directional or coherent noise sources. Source localization, dereverberation, and beamforming in reverberant environments further enable to increase the intelligibility of the near-end speech signal.

Robust Automatic Speech Recognition: A Bridge to Practical Applications establishes a solid foundation for automatic speech recognition that is robust against acoustic environmental distortion. It provides a thorough overview of classical and modern noise-and reverberation robust techniques that have been developed over the past thirty years, with an emphasis on practical methods that have been proven to be successful and which are likely to be further developed for future applications.The strengths and weaknesses of robustness-enhancing speech recognition techniques are carefully analyzed. The book covers noise-robust techniques designed for acoustic models which are based on both Gaussian mixture models and deep neural networks. In addition, a guide to selecting the best methods for practical applications is provided.The reader will: - Gain a unified, deep and systematic understanding of the state-of-the-art technologies for robust speech recognition - Learn the links and relationship between alternative technologies for robust speech recognition - Be able to use the technology analysis and categorization detailed in the book to guide future technology development - Be able to develop new noise-robust methods in the current era of deep learning for acoustic modeling in speech recognition - The first book that provides a comprehensive review on noise and reverberation robust speech recognition methods in the era of deep neural networks - Connects robust speech recognition techniques to machine learning paradigms with rigorous mathematical treatment - Provides elegant and structural ways to categorize and analyze noise-robust speech recognition techniques - Written by leading researchers who have been actively working on the subject matter in both industrial and academic organizations for many years

The Handbook of Natural Language Processing, Second Edition presents practical tools and techniques for implementing natural language processing in computer systems. Along with removing outdated material, this edition updates every chapter and expands the content to include emerging areas, such as sentiment analysis.New to the Second EditionGreater

Provides the reader with a practical introduction to the wide range of important concepts that comprise the field of digital speech processing. Students of speech research and researchers working in the field can use this as a reference guide.