This book is one outcome of the NATO Advanced Studies Institute (ASI) Workshop, "Speechreading by Man and Machine," held at the Chateau de Bonas, Castera-Verduzan (near Auch, France) from August 28 to Septem ber 8, 1995 - the first interdisciplinary meeting devoted the subject of speechreading ("lipreading"). The forty-five attendees from twelve countries covered the gamut of speechreading research, from brain scans of humans processing bi-modal stimuli, to psychophysical experiments and illusions, to statistics of comprehension by the normal and deaf communities, to models of human perception, to computer vision and learning algorithms and hardware for automated speechreading machines. The first week focussed on speechreading by humans, the second week by machines, a general organization that is preserved in this volume. After the in evitable difficulties in clarifying language and terminology across disciplines as diverse as human neurophysiology, audiology, psychology, electrical en gineering, mathematics, and computer science, the participants engaged in lively discussion and debate. We think it is fair to say that there was an atmosphere of excitement and optimism for a field that is both fascinating and potentially lucrative. Of the many general results that can be taken from the workshop, two of the key ones are these: • The ways in which humans employ visual image for speech recogni tion are manifold and complex, and depend upon the talker-perceiver pair, severity and age of onset of any hearing loss, whether the topic of conversation is known or unknown, the level of noise, and so forth.
This book presents the most recent achievements in some rapidly developing fields within Computer Science. This includes the very latest research in biometrics and computer security systems, and descriptions of the latest inroads in artificial intelligence applications. The book contains over 30 articles by well-known scientists and engineers. The articles are extended versions of works introduced at the ACS-CISIM 2005 conference.
This book addresses state-of-the-art systems and achievements in various topics in the research field of speech and language technologies. Book chapters are organized in different sections covering diverse problems, which have to be solved in speech recognition and language understanding systems. In the first section machine translation systems based on large parallel corpora using rule-based and statistical-based translation methods are presented. The third chapter presents work on real time two way speech-to-speech translation systems. In the second section two papers explore the use of speech technologies in language learning. The third section presents a work on language modeling used for speech recognition. The chapters in section Text-to-speech systems and emotional speech describe corpus-based speech synthesis and highlight the importance of speech prosody in speech recognition. In the fifth section the problem of speaker diarization is addressed. The last section presents various topics in speech technology applications like audio-visual speech recognition and lip reading systems.
Intelligent Speech Signal Processing investigates the utilization of speech analytics across several systems and real-world activities, including sharing data analytics, creating collaboration networks between several participants, and implementing video-conferencing in different application areas. Chapters focus on the latest applications of speech data analysis and management tools across different recording systems. The book emphasizes the multidisciplinary nature of the field, presenting different applications and challenges with extensive studies on the design, development and management of intelligent systems, neural networks and related machine learning techniques for speech signal processing.
This is the first volume of proceedings including selected papers from the International Conference on IT Convergence and Security (ICITCS) 2017, presenting a snapshot of the latest issues encountered in this field. It explores how IT convergence and security issues are core to most current research, and industrial and commercial activities. It consists of contributions covering topics such as machine learning & deep learning, communication and signal processing, computer vision and applications, future network technology, artificial intelligence and robotics. ICITCS 2017 is the latest in a series of highly successful International Conferences on IT Convergence and Security, previously held in Prague, Czech Republic(2016), Kuala Lumpur, Malaysia (2015) Beijing, China (2014), Macau, China (2013), Pyeong Chang, Korea (2012), and Suwon, Korea (2011).
Theoretical results suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one may need deep architectures. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers or in complicated propositional formulae re-using many sub-formulae. Searching the parameter space of deep architectures is a difficult task, but learning algorithms such as those for Deep Belief Networks have recently been proposed to tackle this problem with notable success, beating the state-of-the-art in certain areas. This paper discusses the motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models such as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks.
What is Audio Visual Speech Recognition Audio visual speech recognition (AVSR) is a technique that uses image processing capabilities in lip reading to aid speech recognition systems in recognizing undeterministic phones or giving preponderance among near probability decisions. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Audio-visual speech recognition Chapter 2: Data compression Chapter 3: Speech recognition Chapter 4: Speech synthesis Chapter 5: Affective computing Chapter 6: Spectrogram Chapter 7: Lip reading Chapter 8: Face detection Chapter 9: Feature (machine learning) Chapter 10: Statistical classification (II) Answering the public top questions about audio visual speech recognition. (III) Real world examples for the usage of audio visual speech recognition in many fields. 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 Audio Visual Speech Recognition.
This book presents new theories and working models in the area of data analytics and learning. The papers included in this volume were presented at the first International Conference on Data Analytics and Learning (DAL 2018), which was hosted by the Department of Studies in Computer Science, University of Mysore, India on 30–31 March 2018. The areas covered include pattern recognition, image processing, deep learning, computer vision, data analytics, machine learning, artificial intelligence, and intelligent systems. As such, the book offers a valuable resource for researchers and practitioners alike.
A complete overview of distant automatic speech recognition The performance of conventional Automatic Speech Recognition (ASR) systems degrades dramatically as soon as the microphone is moved away from the mouth of the speaker. This is due to a broad variety of effects such as background noise, overlapping speech from other speakers, and reverberation. While traditional ASR systems underperform for speech captured with far-field sensors, there are a number of novel techniques within the recognition system as well as techniques developed in other areas of signal processing that can mitigate the deleterious effects of noise and reverberation, as well as separating speech from overlapping speakers. Distant Speech Recognitionpresents a contemporary and comprehensive description of both theoretic abstraction and practical issues inherent in the distant ASR problem. Key Features: Covers the entire topic of distant ASR and offers practical solutions to overcome the problems related to it Provides documentation and sample scripts to enable readers to construct state-of-the-art distant speech recognition systems Gives relevant background information in acoustics and filter techniques, Explains the extraction and enhancement of classification relevant speech features Describes maximum likelihood as well as discriminative parameter estimation, and maximum likelihood normalization techniques Discusses the use of multi-microphone configurations for speaker tracking and channel combination Presents several applications of the methods and technologies described in this book Accompanying website with open source software and tools to construct state-of-the-art distant speech recognition systems This reference will be an invaluable resource for researchers, developers, engineers and other professionals, as well as advanced students in speech technology, signal processing, acoustics, statistics and artificial intelligence fields.