A typical speech recognition system is push button operated (Push-to-talk), which requires hand movement and hence mixed multi-modal interface. However, for disabled patients and those who use hands-busy applications (e.g., where the user has objects to manipulate or device to control while asking for assistance from another device) movement may be restricted or impossible. The only alternative is to use Speech Only Interface. The method that is being proposed is called Wake-Up-Word Speech Recognition (WUW-SR). A WUW-SR system would allow the user to operate (activate) many systems (Cell phone, Computer, Elevator, etc.) with speech commands instead of hand movements. This work defines a new front-end paradigm of the Wake-Up-Word Speech Recognition on Field Programmable gate Arrays (FPGA). The-State-of-The-Art Front-end of WUW-SR system is based on three different subsystems that can produce three sets of features simultaneously: Mel-frequency Cepstral Coefficients (MFCC), Linear Predictive Coding Coefficients (LPC), and Enhanced Mel-frequency Cepstral Coefficients (ENH_MFCC). These extracted features are then compressed and transmitted to the server via a dedicated channel, where subsequently they are decoded. These features are decoded with corresponding Hidden Markov Models (HMMs) in the back-end stage of the WUW-SR. In the WUW-SR system, the front-end processor is located at the terminal (e.g. hand-held device) which is typically connected over a data network to remote back-end recognition (e.g., server). WUW's front-end can be added to any hand-held electronic device compatible with WUW-SR and command (activate) it by using our voice only (no push to talk as is presently done). WUW's front-end is designed, and implemented in Altera DSP development kit with Cyclone III FPGA as a portable system acting as a processor that is capable of computing three different sets of features at a much faster rate than software. It is cost effective, consumes very little power, and it is not limited by having to operate on a general-purpose computer so it can be used on any portable device.

This book introduced an approach to design and implement an embedded SoPC (System on Programmable Chip) technique with Altera Nios II processor on a FPGA chip for real-time speech recognition system by developing hardware/software with minimum usage of resources (hardware components) and relatively small size software. This reduces the memory utilization, achieved by using Mel Frequency Cepstral Coefficients (MFCCs) technique as feature extraction combined with its first derivative ( MFCCs) including power computation of the speech frames (i.e. E, MFCC, E, and MFCC), called observation vector of the speech signal. To model the obtained observation, Gaussian Mixture Model (GMM) has been used, which is passed to a Hidden Markov Model (HMM) as probabilistic model to process the GMM statistically to take a decision on the uttered words recognition, whether a single or composite, one or more syllable words (i.e. one, six, welcome). The words that are used for training and testing the system included selected English and Arabic words.

Speech recognition is a computationally demanding task, especially the decoding part, which converts pre-processed speech data into words or sub-word units, and which incorporates Viterbi decoding and Gaussian distribution calculations. In this thesis, this part of the recognition process is implemented in programmable logic, specifically, on a field-programmable gate array (FPGA). Relevant background material about speech recognition is presented, along with a critical review of previous hardware implementations. Designs for a decoder suitable for implementation in hardware are then described. These include details of how multiple speech files can be processed in parallel, and an original implementation of an algorithm for summing Gaussian mixture components in the log domain. These designs are then implemented on an FPGA. An assessment is made as to how appropriate it is to use hardware for speech recognition. It is concluded that while certain parts of the recognition algorithm are not well suited to this medium, much of it is, and so an efficient implementation is possible. Also presented is an original analysis of the requirements of speech recognition for hardware and software, which relates the parameters that dictate the complexity of the system to processing speed and bandwidth. The FPGA implementations are compared to equivalent software, written for that purpose. For a contemporary FPGA and processor, the FPGA outperforms the software by an order of magnitude.

This book addresses different aspects of the research field and a wide range of topics in speech signal processing, speech recognition and language processing. The chapters are divided in three different sections: Speech Signal Modeling, Speech Recognition and Applications. The chapters in the first section cover some essential topics in speech signal processing used for building speech recognition as well as for speech synthesis systems: speech feature enhancement, speech feature vector dimensionality reduction, segmentation of speech frames into phonetic segments. The chapters of the second part cover speech recognition methods and techniques used to read speech from various speech databases and broadcast news recognition for English and non-English languages. The third section of the book presents various speech technology applications used for body conducted speech recognition, hearing impairment, multimodal interfaces and facial expression recognition.

Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary. Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures Work with Arduino and ultra-low-power microcontrollers Learn the essentials of ML and how to train your own models Train models to understand audio, image, and accelerometer data Explore TensorFlow Lite for Microcontrollers, Google’s toolkit for TinyML Debug applications and provide safeguards for privacy and security Optimize latency, energy usage, and model and binary size

Field Programmable Gate Arrays (FPGAs) are devices that provide a fast, low-cost way for embedded system designers to customize products and deliver new versions with upgraded features, because they can handle very complicated functions, and be reconfigured an infinite number of times. In addition to introducing the various architectural features available in the latest generation of FPGAs, The Design Warrior's Guide to FPGAs also covers different design tools and flows.This book covers information ranging from schematic-driven entry, through traditional HDL/RTL-based simulation and logic synthesis, all the way up to the current state-of-the-art in pure C/C++ design capture and synthesis technology. Also discussed are specialist areas such as mixed hardward/software and DSP-based design flows, along with innovative new devices such as field programmable node arrays (FPNAs). Clive "Max" Maxfield is a bestselling author and engineer with a large following in the electronic design automation (EDA)and embedded systems industry. In this comprehensive book, he covers all the issues of interest to designers working with, or contemplating a move to, FPGAs in their product designs. While other books cover fragments of FPGA technology or applications this is the first to focus exclusively and comprehensively on FPGA use for embedded systems. - First book to focus exclusively and comprehensively on FPGA use in embedded designs - World-renowned best-selling author - Will help engineers get familiar and succeed with this new technology by providing much-needed advice on choosing the right FPGA for any design project

This practical, tool-independent guide to designing digital circuits takes a unique, top-down approach, reflecting the nature of the design process in industry. Starting with architecture design, the book comprehensively explains the why and how of digital circuit design, using the physics designers need to know, and no more.

This book provides insights into how deep learning techniques impact language and speech processing applications. The authors discuss the promise, limits and the new challenges in deep learning. The book covers the major differences between the various applications of deep learning and the classical machine learning techniques. The main objective of the book is to present a comprehensive survey of the major applications and research oriented articles based on deep learning techniques that are focused on natural language and speech signal processing. The book is relevant to academicians, research scholars, industrial experts, scientists and post graduate students working in the field of speech signal and natural language processing and would like to add deep learning to enhance capabilities of their work. Discusses current research challenges and future perspective about how deep learning techniques can be applied to improve NLP and speech processing applications; Presents and escalates the research trends and future direction of language and speech processing; Includes theoretical research, experimental results, and applications of deep learning.

During the 1980s and early 1990s there was signi?cant work in the design and implementation of hardware neurocomputers. Nevertheless, most of these efforts may be judged to have been unsuccessful: at no time have have ha- ware neurocomputers been in wide use. This lack of success may be largely attributed to the fact that earlier work was almost entirely aimed at developing custom neurocomputers, based on ASIC technology, but for such niche - eas this technology was never suf?ciently developed or competitive enough to justify large-scale adoption. On the other hand, gate-arrays of the period m- tioned were never large enough nor fast enough for serious arti?cial-neur- network (ANN) applications. But technology has now improved: the capacity and performance of current FPGAs are such that they present a much more realistic alternative. Consequently neurocomputers based on FPGAs are now a much more practical proposition than they have been in the past. This book summarizes some work towards this goal and consists of 12 papers that were selected, after review, from a number of submissions. The book is nominally divided into three parts: Chapters 1 through 4 deal with foundational issues; Chapters 5 through 11 deal with a variety of implementations; and Chapter 12 looks at the lessons learned from a large-scale project and also reconsiders design issues in light of current and future technology.

Today, embedded systems are widely deployed in just about every piece of machinery from toasters to spacecrafts, and embedded system designers face many challenges. They are asked to produce increasingly complex systems using the latest technologies, but these technologies are changing faster than ever. They are asked to produce better quality designs with a shorter time-to-market. They are asked to implement increasingly complex functionality but, more importantly, to satisfy numerous other constraints. To achieve these current goals, the designer must be aware of such design constraints and, more importantly, the factors that have a direct effect on them. One of the challenges facing embedded system designers is the selection of the optimum processor for the application in hand: single-purpose, general-purpose, or application specific. Microcontrollers are one member of the family of the application specific processors. Digital System Design concentrates on the use of a microcontroller as the embedded system's processor and how to use it in many embedded system applications. The book covers both the hardware and software aspects needed to design using microcontrollers and is ideal for undergraduate students and engineers that are working in the field of digital system design.