It is a little known fact that reading was taught by means of spelling for over 200 years. Today the impact of spelling on reading achievement is not as well appreciated as it once was. The late Dr. Ronald P. Carver did extensive research into the causal relationships between spelling instruction and reading ability. Carver concluded, "One very important way to learn how to pronounce more words accurately is sometimes overlooked, that is, learning to spell more words accurately." (Causes of High and Low Reading Achievement, p. 178). He also notes that "spelling was used to teach reading for almost 200 years, but by the beginning of the 20th century, the tide had so turned that learning to spell was largely seen as incidental to learning to read." Quoting C. A. Perfetti, Carver observed, "practice at spelling should help reading more than practice of reading helps spelling." (p. 179. In June of 2004 Miss Geraldine Rodgers sent me her essay, "Why Noah Webster's Way Was the Right Way." She argued from the history of reading and the psychology of reading that Webster's spelling book method of teaching reading and spelling was superior to all other methods. I was surprised to learn that that Webster, in his 1828 American Dictionary of the English Language, defined a Spelling Book as, " A book for teaching children to spell and read." He also wrote under the entry, Spelling, "To tell the name of letters of a word, with a proper division of syllables, for the purpose of learning the pronunciation. In this manner children learn to read by first spelling the words." You can see that Webster was quite clear about the dual purpose of the spelling books in his day. You can imagine my surprise at the improvement I began to get with my tutoring students when they started working through Webster's Spelling Book. I decided to type up my own edition to use in my private tutoring and my tutoring work at the Odessa Christian School in Odessa, TX, where I teach remedial reading and Spanish. In this edition, I have retained everything in the original 1908 (descendant from the 1829 edition). The only differences relate to formatting. I chose to list the words in rows instead of columns. I also allow the words to divide at the ends of lines. I have found that this works fine for all students. We are teaching students to read and spell by syllables and not by word shapes or context. When reading and spelling are taught by the Spelling Book Method, all guessing at words from shape or context is completely eliminated. The student's total focus is on pronouncing the words correctly, high levels of comprehension are a natural result.

We live in a noisy world! In all applications (telecommunications, hands-free communications, recording, human-machine interfaces, etc.) that require at least one microphone, the signal of interest is usually contaminated by noise and reverberation. As a result, the microphone signal has to be "cleaned" with digital signal processing tools before it is played out, transmitted, or stored. This book is about speech enhancement. Different well-known and state-of-the-art methods for noise reduction, with one or multiple microphones, are discussed. By speech enhancement, we mean not only noise reduction but also dereverberation and separation of independent signals. These topics are also covered in this book. However, the general emphasis is on noise reduction because of the large number of applications that can benefit from this technology. The goal of this book is to provide a strong reference for researchers, engineers, and graduate students who are interested in the problem of signal and speech enhancement. To do so, we invited well-known experts to contribute chapters covering the state of the art in this focused field. TOC:Introduction.- Study of the Wiener Filter for Noise Reduction.- Statistical Methods for the Enhancement of Noisy Speech.- Single- und Multi-Microphone Spectral Amplitude Estimation Using a Super-Gaussian Speech Model.- From Volatility Modeling of Financial Time-Series to Stochastic Modeling and Enhancement of Speech Signals.- Single-Microphone Noise Suppression for 3G Handsets Based on Weighted Noise Estimation.- Signal Subspace Techniques for Speech Enhancement.- Speech Enhancement: Application of the Kalman Filter in the Estimate-Maximize (EM) Framework.- Speech Distortion Weighted Multichannel Wiener Filtering Techniques for Noise Reduction.- Adpative Microphone Arrays Employing Spatial Quadratic Soft Constraints and Spectral Shaping.- Single-Microphone Blind Dereverberation.- Separation and Dereverberation of Speech Signals with Multiple Microphones.- Frequency-Domain Blind Source Separation.- Subband Based Blind Source Separation.- Real-Time Blind Source Separation for Moving Speech Signals.- Separation of Speech by Computational Auditory Scene Analysis

This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since federated learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR. This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated learning model against different types of attacks such as data leakage and/or data poisoning. Secondly, the book presents incentive mechanisms which aim to encourage individuals to participate in the federated learning ecosystems. Last but not least, this book also describes how federated learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both the academia and the industry, who would like to learn about federated learning, practice its implementation, and apply it in their own business. Readers are expected to have some basic understanding of linear algebra, calculus, and neural network. Additionally, domain knowledge in FinTech and marketing would be helpful.”