João Freitas holds a Degree in Computer Engineering (2007) from the Polytechnic Institute of Lisbon, Portugal and a PhD in Computer Science (2015) from the Universities of Minho, Porto and Aveiro. He works at the Microsoft Language Development Center (Lisbon, Portugal) as a Researcher and Lead Software Engineer since 2006, where he has participated in several R&D national and international projects in the areas of Speech Recognition and Synthesis, Crowd-Sourcing Data Collections and Ambient Assisted Living. During this time he has also acquired experience in software development, quality assurance and data analysis. His main research activity is in the area of Silent Speech Interfaces in which he co- authored several international peer reviewed publications (book chapters, international journals and proceedings of international conferences). He also has interests in other areas of research such as Human-Computer Interaction and Computer Vision.
António Teixeira, Associate Professor (with tenure) at University of Aveiro, Portugal, PhD in Speech Synthesis and Master in Electronics and Telecommunications. Chair of the Special Interest Group on Iberian Languages (SIG-IL) of the International Speech Communication Association (ISCA). Director of the Master in Speech and Hearing Sciences at University of Aveiro since 2004. Senior researcher at IEETA. His main research activity is in the areas of Multimodal Human-Machine Interaction and Speech Processing. Large experience in spoken language interaction; architectures, new applications and services for AAL; speech technologies and information extraction applications. He is/was involved in several national and European research projects, being project responsible or co-responsible of University of Aveiro, participation in Heron II, Living Usability Lab (LUL), AAL4ALL, Smartphones for Seniors, AAL PaeLIFE and Marie Curie IAPP IRIS; research since 1998 in spoken interaction with robots, including participation in project CARL: Communication, Action, Reasoning and Learning in Robotics; has supervised 8 concluded PhDs (including João Freitas PhD defended in 2015) and 50 Masters; and co-authored more than two hundred publications (books, book chapters, international journals and proceedings of international conferences).
Miguel Sales Dias holds a bachelor (1985) and a master (1988) in Electrical and Computer Engineering (IST-UTL, Portugal) and a PhD in Computer Graphics and Multimedia (1998) from ISCTE-IUL where he was an Associated Professor until 2005, holding currently an Invited Associated Professor position, teaching and conducting research in Computer Graphics, Virtual and Augmented reality, Ambient Assisted Living and Multimodal Human-Computer Interaction. Since November 2005, he is the Director of the first European R&D Centre in Speech and Natural User Interaction Technologies of Microsoft Corporation in Portugal (Microsoft Language Development Center, MLDC). He is regularly commissioned by the European Commission for R&D project evaluations and reviews. Author of 1 patent, author, co-author or editor of 11 scientific books or journal editions, 12 indexed papers in international journals, 26 chapters in indexed international books, 144 other publications, workshops or keynotes in international conferences. Since 1992 he has participated or participates in 33 International R&D projects (ESPRIT, RACE, ACTS, TELEMATICS, TEN-IBC, EUREKA, INTERREG, FP5 IST-IPS, FP6 IST, ESA, Marie Curie, AAL, ACP), and 15 National (FCT, QREN, NITEC, POSC, POCTI, POSI, ICPME, TIT). He has supervised 5 concluded Phds. He obtained 5 scientific prizes. He is a member of ACM SIGGRAPH, Eurographics, ISCA and IEEE; editorial boards of several journal; several Program Committees of National and International conferences in Computer Graphics, Virtual and Augmented Reality, Speech technologies, Accessibility and Ambient Assisted Living. He was President of ADETTI, an ISCTE-IUL associated R&D research center. He was Vice-president and Secretary of the Portuguese Group of Computer Graphics, Eurographics Portuguese Chapter.
Samuel Silva graduated in Electronics and Telecommunications Engineering in 2003, completed an MSc (pre-Bologna) in the same area in 2007 and obtained his PhD in Informatics Engineering in 2012, all by University of Aveiro. His main research interests include medical imaging processing and analysis, data and information visualization, and human computer interaction, areas in which he (co-) authored eleven articles in international peer reviewed journals and more than 50 papers in international peer reviewed conferences. As a researcher, at the Institute of Electronics and Informatics Engineering of Aveiro (Portugal), S. Silva has been actively involved in several national and international R&D projects spanning the areas of Ambient Assisted Living, Multimodal Interaction and Speech Production.
The book is appropriate for scientists and researchers in the field of speech recognition who will find an overview of the state of the art in robust speech recognition, professionals working in speech recognition who will find strategies for improving recognition results in various conditions of mismatch, and lecturers of advanced courses on speech processing or speech recognition who will find a reference and a comprehensive introduction to the field. The book assumes an understanding of the fundamentals of speech recognition using Hidden Markov Models.
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 presents the enabling technology for such systems. It introduces a variety of methods and techniques to implement an individualized, adaptive, flexible, and robust behavior for technical systems by means of cognitive processes, including perception, cognition, interaction, planning, and reasoning. The technological developments are complemented by empirical studies from psychological and neurobiological perspectives.
· Provides an overview of state-of-the-art technology in information extraction (IE), discussing achievements and limitations for the software developer and providing references for specialized literature in the area
· Presents a comprehensive list of freely available, high quality software for several subtasks of IE and for several natural languages
· Describes a generic architecture that can learn how to extract information for a given application domain
Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you’ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research.Develop a naïve Bayesian classifier to determine if an email is spam, based only on its textUse linear regression to predict the number of page views for the top 1,000 websitesLearn optimization techniques by attempting to break a simple letter cipherCompare and contrast U.S. Senators statistically, based on their voting recordsBuild a “whom to follow” recommendation system from Twitter data