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"This E-book is a collection of articles that describe advances in speech recognition technology. Robustness in speech recognition refers to the need to maintain high speech recognition accuracy even when the quality of the input speech is degraded, or whe"
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
Foreword Looking back the past 30 years. we have seen steady progress made in the area of speech science and technology. I still remember the excitement in the late seventies when Texas Instruments came up with a toy named "Speak-and-Spell" which was based on a VLSI chip containing the state-of-the-art linear prediction synthesizer. This caused a speech technology fever among the electronics industry. Particularly. applications of automatic speech recognition were rigorously attempt ed by many companies. some of which were start-ups founded just for this purpose. Unfortunately. it did not take long before they realized that automatic speech rec ognition technology was not mature enough to satisfy the need of customers. The fever gradually faded away. In the meantime. constant efforts have been made by many researchers and engi neers to improve the automatic speech recognition technology. Hardware capabilities have advanced impressively since that time. In the past few years. we have been witnessing and experiencing the advent of the "Information Revolution." What might be called the second surge of interest to com mercialize speech technology as a natural interface for man-machine communication began in much better shape than the first one. With computers much more powerful and faster. many applications look realistic this time. However. there are still tremendous practical issues to be overcome in order for speech to be truly the most natural interface between humans and machines.
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.
Automatic speech recognition (ASR) systems are finding increasing use in everyday life. Many of the commonplace environments where the systems are used are noisy, for example users calling up a voice search system from a busy cafeteria or a street. This can result in degraded speech recordings and adversely affect the performance of speech recognition systems. As the use of ASR systems increases, knowledge of the state-of-the-art in techniques to deal with such problems becomes critical to system and application engineers and researchers who work with or on ASR technologies. This book presents a comprehensive survey of the state-of-the-art in techniques used to improve the robustness of speech recognition systems to these degrading external influences. Key features: Reviews all the main noise robust ASR approaches, including signal separation, voice activity detection, robust feature extraction, model compensation and adaptation, missing data techniques and recognition of reverberant speech. Acts as a timely exposition of the topic in light of more widespread use in the future of ASR technology in challenging environments. Addresses robustness issues and signal degradation which are both key requirements for practitioners of ASR. Includes contributions from top ASR researchers from leading research units in the field
A study of digital speech processing, synthesis and recognition. This second edition contains new sections on the international standardization of robust and flexible speech coding techniques, waveform unit concatenation-based speech synthesis, large vocabulary continuous-speech recognition based on statistical pattern recognition, and more.
In recent decades Multimedia processing has emerged as an important technology to generate content based on images, video, audio, graphics, and text. This book is a compilation of the latest trends and developments in the field of computational intelligence in multimedia processing. The edited book presents a large number of interesting applications to intelligent multimedia processing of various Computational Intelligence techniques including neural networks and fuzzy logic.
This book covers the state-of-the-art in deep neural-network-based methods for noise robustness in distant speech recognition applications. It provides insights and detailed descriptions of some of the new concepts and key technologies in the field, including novel architectures for speech enhancement, microphone arrays, robust features, acoustic model adaptation, training data augmentation, and training criteria. The contributed chapters also include descriptions of real-world applications, benchmark tools and datasets widely used in the field. This book is intended for researchers and practitioners working in the field of speech processing and recognition who are interested in the latest deep learning techniques for noise robustness. It will also be of interest to graduate students in electrical engineering or computer science, who will find it a useful guide to this field of research.
This book includes selected contributions related to big data and data networking, presented at the 13th International Conference on Computing and Information Technology (IC2IT), which was held at the Arnoma Grand Hotel Bangkok, Thailand, July 6–7, 2017. The aim of the conference was to present emerging algorithms, methods and technologies with a high degree of originality, novelty and innovation addressing the conference theme `Mastering Data and Networking’. Section 1 and 2 discuss various aspects of data mining and corresponding applications. Section 3 focuses on speed and overhead networking optimisation problems, as well as energy problems of autonomous systems, which are becoming increasingly important. The key to addressing these problems is properly determining critical parameters. Section 4 sheds light on natural language processing, including extraction of trends and popularity and recognition of emotions as well as classic topics such as detection and classification.
International Conference on Advances in Pattern Recognition (ICAPR 98) at Plymouth represents an important meeting for advanced research in pattern recognition. There is considerable interest in the areas of image processing, medical imaging, speech recognition, document analysis and character recognition, fuzzy data analysis and neural networks. ICAPR 98 is aimed at providing an international platform for invited research in this multi-disciplinary area. It is expected that the conference will grow in future years to include more research contributions that detail state-of the-art research in pattern recognition. ICAPR 98 attracted contributions from different countries of the highest quality. I should like to thank the programme and organising committee for doing an excellent job in organising this conference. The peer reviewed nature of the conference ensured high quality publications in these proceedings. My personal thanks to Mrs. Barbara Davies who served as conference secretary and worked tirelessly in organising the conference. I thank the organising chair for the local arrangements and our should also key-note, plenary and tutorial speakers for their valuable contributions to the conference. I also thank Springer-Verlag for publishing these proceedings that will be a valuable source of research reference for the readers. Finally, I thank all participants who made this conference successful.