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"Speech processing systems Computer programs."
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Dragon NaturallySpeaking for dummies
Command your computer, surf the web, create reports, and more-- with your voice! Dragon NaturallySpeaking is a speech recognition program that lets users dictate into any Windows application, allowing you to access documents, write e-mails, and even update Facebook using only your voice.
Book
Dragon NaturallySpeaking for Dummies
Learn to speak Dragon with For Dummies! Fully updated for the latest speech recognition tools and features, this bestselling guide helps you conquer Dragon NaturallySpeaking and gets you started creating documents, sending e-mail, searching the web, and more using only your voice. You?ll learn Dragon basics like dictation, formatting, and proofreading before moving on to more advanced options including posting to social media sites, developing custom commands, troubleshooting, and improving communication with Dragon. Explains the ins and outs of voice-recognition software and the wealth of possibilities available in Dragon NaturallySpeaking Discusses how Dragon NaturallySpeaking allows you to interact with and command you PC, cruise through e-mail, update Facebook, surf the web, and create reports just by speaking Shows you how to increase your productivity at work and stay organized on the go with Dragon NaturallySpeaking Reveals time-saving tips, mistakes to avoid, and the most common issues you may encounter when getting started with Dragon NaturallySpeaking Stop \"dragon\" your feet and get started with Dragon Naturally Speaking For Dummies, 3rd Edition today!.
eBook
Internet of things and smart environments : assistive technologies for disability, dementia, and aging
This book is focused on the Internet of Things (IoT) services and smart environments that can be of assistance to the elderly and individuals living with dementia or some sensory impairment.The book outlines the requirements of the systems that aim to furnish some digital sensory or cognitive assistance to the individuals and their caregivers.
eBook
Constructive Dialogue Modelling
Constructive Dialogue Modelling: Speech Interaction and Rational Agents provides an overview of the current dialogue technology and research trends in spoken dialogue systems, presenting a coherent perspective of AI-based cooperative interaction management. The book complements existing research regarding human-computer interfaces, speech and language technology, and communication studies in general, bringing different view-points together and integrating them into a single point of reference. Key Features: •Presents a guide to spoken dialogue technology and current research trends. •Provides an overview of human factors in dialogue systems and delivers a new metaphor for human-computer interaction and computer as agent. •Explains the architecture of dialogue systems using examples from systems such as Interact and DUMAS •Offers a comprehensive overview of original research into the new trends in speech dialogue technology in light of innovations such as ubiquitous computing. This book will provide essential reading for industrial designers and interface engineers, university researchers and teachers, computer scientists, human communication researchers, speech and language technologists, cognitive engineers/cognitive scientists, as well as social and media researchers, and psychologists. Advanced students and researchers in computer science, speech and language technologies, psychology and communication research will find this text of interest.
eBook
Visual computing for medicine : theory, algorithms, and applications
Visual Computing for Medicine, Second Edition, offers cutting-edge visualization techniques and their applications in medical diagnosis, education, and treatment. The book includes algorithms, applications, and ideas on achieving reliability of results and clinical evaluation of the techniques covered. Preim and Botha illustrate visualization techniques from research, but also cover the information required to solve practical clinical problems. They base the book on several years of combined teaching and research experience. This new edition includes six new chapters on treatment planning, guidance and training; an updated appendix on software support for visual computing for medicine; and a new global structure that better classifies and explains the major lines of work in the field. Complete guide to visual computing in medicine, fully revamped and updated with new developments in the fieldIllustrated in full colorIncludes a companion website offering additional content for professors, source code, algorithms, tutorials, videos, exercises, lessons, and more
eBook
Bots in software engineering: a systematic mapping study
Bots have emerged from research prototypes to deployable systems due to the recent developments in machine learning, natural language processing and understanding techniques. In software engineering, bots range from simple automated scripts to decision-making autonomous systems. The spectrum of applications of bots in software engineering is so wide and diverse, that a comprehensive overview and categorization of such bots is needed. Existing works considered selective bots to be analyzed and failed to provide the overall picture. Hence it is significant to categorize bots in software engineering through analyzing why, what and how the bots are applied in software engineering. We approach the problem with a systematic mapping study based on the research articles published in this topic. This study focuses on classification of bots used in software engineering, the various dimensions of the characteristics, the more frequently researched area, potential research spaces to be explored and the perception of bots in the developer community. This study aims to provide an introduction and a broad overview of bots used in software engineering. Discussions of the feedback and results from several studies provide interesting insights and prospective future directions.
Journal Article
Music Training Increases Phonological Awareness and Reading Skills in Developmental Dyslexia: A Randomized Control Trial
There is some evidence for a role of music training in boosting phonological awareness, word segmentation, working memory, as well as reading abilities in children with typical development. Poor performance in tasks requiring temporal processing, rhythm perception and sensorimotor synchronization seems to be a crucial factor underlying dyslexia in children. Interestingly, children with dyslexia show deficits in temporal processing, both in language and in music. Within this framework, we test the hypothesis that music training, by improving temporal processing and rhythm abilities, improves phonological awareness and reading skills in children with dyslexia. The study is a prospective, multicenter, open randomized controlled trial, consisting of test, rehabilitation and re-test (ID NCT02316873). After rehabilitation, the music group (N = 24) performed better than the control group (N = 22) in tasks assessing rhythmic abilities, phonological awareness and reading skills. This is the first randomized control trial testing the effect of music training in enhancing phonological and reading abilities in children with dyslexia. The findings show that music training can modify reading and phonological abilities even when these skills are severely impaired. Through the enhancement of temporal processing and rhythmic skills, music might become an important tool in both remediation and early intervention programs.Trial Registration: ClinicalTrials.gov NCT02316873
Journal Article
A Novel Speech to Mouth Articulation System for Realistic Humanoid Robots
A significant ongoing issue in realistic humanoid robotics (RHRs) is inaccurate speech to mouth synchronisation. Even the most advanced robotic systems cannot authentically emulate the natural movements of the human jaw, lips and tongue during verbal communication. These visual and functional irregularities have the potential to propagate the Uncanny Valley Effect (UVE) and reduce speech understanding in human-robot interaction (HRI). This paper outlines the development and testing of a novel Computer Aided Design (CAD) robotic mouth prototype with buccinator actuators for emulating the fluidic movements of the human mouth. The robotic mouth system incorporates a custom Machine Learning (ML) application that measures the acoustic qualities of speech synthesis (SS) and translates this data into servomotor triangulation for triggering jaw, lip and tongue positions. The objective of this study is to improve current robotic mouth design and provide engineers with a framework for increasing the authenticity, accuracy and communication capabilities of RHRs for HRI. The primary contributions of this study are the engineering of a robotic mouth prototype and the programming of a speech processing application that achieved a 79.4% syllable accuracy, 86.7% lip synchronisation accuracy and 0.1s speech to mouth articulation differential.
Journal Article
Design Space Exploration for Partitioning Dataflow Program on CPU-GPU Heterogeneous System
Dataflow programming is a methodology that enables the development of high-level, parametric programs that are independent of the underlying platform. This approach is particularly useful for heterogeneous platforms, as it eliminates the need to rewrite application software for each configuration. Instead, it only requires new low-level implementation code, which is typically automatically generated through code generation tools. The performance of programs running on heterogeneous parallel platforms is highly dependent on the partitioning and mapping of computation to different processing units. This is determined by parameters that govern the partitioning, mapping, scheduling, and allocation of data exchanges among the processing elements of the platform. Determining the appropriate parameters for a specific application and set of architectures is a complex task and is an active area of research. This paper presents a novel methodology for partitioning and mapping dataflow programs onto heterogeneous systems composed of both CPUs and GPUs. The objective is to identify the program configuration that provides the most efficient way to process a typical dataflow program by exploring its design space. This is an NP-complete problem that we have addressed by utilizing a design space exploration approach that leverages a Tabu search meta-heuristic optimization algorithm driven by analysis of the execution trace graph of the program. The heuristic algorithm effectively identifies a solution that maps actors to processing units while improving overall performance. The parameters of the heuristic algorithm, such as the time limit and the proportion of neighboring solutions explored during each iteration, can be fine-tuned for optimal results. Additionally, the proposed approach allows for the exploration of solutions that do not utilize all hardware resources if it results in better performance. The effectiveness of the proposed approach is demonstrated through experimental results on dataflow programs.
Journal Article
Optimization of Energy and Security in Mobile Sensor Network Using Classification Based Signal Processing in Heterogeneous Network
The use of mobile devices has been growing rapidly and at the same time mobile applications has attained increasing popularity. Moreover, mobile services have introduced unexpected demands on the infrastructure of mobile and wireless networking. Recently, Mobile Sensor Networks (MSNs) has developed a class of popular sensor network where mobility has the major responsibility in executing the application. Since the deployment of a sensor network is not completely static, the mobility factor has plenty of challenges which have to be solved including coverage, connectivity, and energy consumption. The aim of this article is to design a robust procedure to handle the challenges of mobile sensors network by providing a secure and energy-efficient communication. In this article swarms of sensors are able to move while maintaining the optimal distance among sensor nodes.The security and energy of the signal transmission has been enhanced using multi-path link routing protocol (MLRP) and a hybrid-based TEEN (H-TEEN) protocol. Transmitted signalshave been classified via a recurrent neural networks (RNN). The simulation results show that this proposed model is able to effectively detect malicious nodes and balance the energy so that the lifespan of MSN can be prolonged.
Journal Article