Automatic speech recognition (ASR) systems are finding increasing use in everyday life. Many of the commonplace environments where the systems are used are noisy, for example users calling up a voice search system from a busy cafeteria or a street. This can result in degraded speech recordings and adversely affect the performance of speech recognition systems. As the use of ASR systems increases, knowledge of the state-of-the-art in techniques to deal with such problems becomes critical to system and application engineers and researchers who work with or on ASR technologies. This book presents a comprehensive survey of the state-of-the-art in techniques used to improve the robustness of speech recognition systems to these degrading external influences.

Key features: Reviews all the main noise robust ASR approaches, including signal separation, voice activity detection, robust feature extraction, model compensation and adaptation, missing data techniques and recognition of reverberant speech.Acts as a timely exposition of the topic in light of more widespread use in the future of ASR technology in challenging environments.Addresses robustness issues and signal degradation which are both key requirements for practitioners of ASR.Includes contributions from top ASR researchers from leading research units in the field

With this comprehensive guide you will learn how to apply Bayesian machine learning techniques systematically to solve various problems in speech and language processing. A range of statistical models is detailed, from hidden Markov models to Gaussian mixture models, n-gram models and latent topic models, along with applications including automatic speech recognition, speaker verification, and information retrieval. Approximate Bayesian inferences based on MAP, Evidence, Asymptotic, VB, and MCMC approximations are provided as well as full derivations of calculations, useful notations, formulas, and rules. The authors address the difficulties of straightforward applications and provide detailed examples and case studies to demonstrate how you can successfully use practical Bayesian inference methods to improve the performance of information systems. This is an invaluable resource for students, researchers, and industry practitioners working in machine learning, signal processing, and speech and language processing.

This work addresses this problem in the short-time Fourier transform (STFT) domain. We divide the general problem into five basic categories depending on the number of microphones being used and whether the interframe or interband correlation is considered. The first category deals with the single-channel problem where STFT coefficients at different frames and frequency bands are assumed to be independent. In this case, the noise reduction filter in each frequency band is basically a real gain. Since a gain does not improve the signal-to-noise ratio (SNR) for any given subband and frame, the noise reduction is basically achieved by liftering the subbands and frames that are less noisy while weighing down on those that are more noisy. The second category also concerns the single-channel problem. The difference is that now the interframe correlation is taken into account and a filter is applied in each subband instead of just a gain. The advantage of using the interframe correlation is that we can improve not only the long-time fullband SNR, but the frame-wise subband SNR as well. The third and fourth classes discuss the problem of multichannel noise reduction in the STFT domain with and without interframe correlation, respectively. In the last category, we consider the interband correlation in the design of the noise reduction filters. We illustrate the basic principle for the single-channel case as an example, while this concept can be generalized to other scenarios. In all categories, we propose different optimization cost functions from which we derive the optimal filters and we also define the performance measures that help analyzing them.

We live in a society which is increasingly interconnected, in which communication between individuals is mostly mediated via some electronic platform, and transactions are often carried out remotely. In such a world, traditional notions of trust and confidence in the identity of those with whom we are interacting, taken for granted in the past, can be much less reliable. Biometrics - the scientific discipline of identifying individuals by means of the measurement of unique personal attributes - provides a reliable means of establishing or confirming an individual's identity. These attributes include facial appearance, fingerprints, iris patterning, the voice, the way we write, or even the way we walk. The new technologies of biometrics have a wide range of practical applications, from securing mobile phones and laptops to establishing identity in bank transactions, travel documents, and national identity cards. This Very Short Introduction considers the capabilities of biometrics-based identity checking, from first principles to the practicalities of using different types of identification data. Michael Fairhurst looks at the basic techniques in use today, ongoing developments in system design, and emerging technologies, all aimed at improving precision in identification, and providing solutions to an increasingly wide range of practical problems. Considering how they may continue to develop in the future, Fairhurst explores the benefits and limitations of these pervasive and powerful technologies, and how they can effectively support our increasingly interconnected society. ABOUT THE SERIES: The Very Short Introductions series from Oxford University Press contains hundreds of titles in almost every subject area. These pocket-sized books are the perfect way to get ahead in a new subject quickly. Our expert authors combine facts, analysis, perspective, new ideas, and enthusiasm to make interesting and challenging topics highly readable.

This book offers an overview of audio processing, including the latest advances in the methodologies used in audio processing and speech recognition. First, it discusses the importance of audio indexing and classical information retrieval problem and presents two major indexing techniques, namely Large Vocabulary Continuous Speech Recognition (LVCSR) and Phonetic Search. It then offers brief insights into the human speech production system and its modeling, which are required to produce artificial speech. It also discusses various components of an automatic speech recognition (ASR) system. Describing the chronological developments in ASR systems, and briefly examining the statistical models used in ASR as well as the related mathematical deductions, the book summarizes a number of state-of-the-art classification techniques and their application in audio/speech classification. By providing insights into various aspects of audio/speech processing and speech recognition, this book appeals a wide audience, from researchers and postgraduate students to those new to the field.

Develop intelligent voice-empowered applications and Chatbots that not only understand voice commands but also respond to it Key Features Target multiple platforms by creating voice interactions for your applications Explore real-world examples of how to produce smart and practical virtual assistants Build a virtual assistant for cars using Android Auto in Xamarin Book DescriptionFrom touchscreen and mouse-click, we are moving to voice- and conversation-based user interfaces. By adopting Voice User Interfaces (VUIs), you can create a more compelling and engaging experience for your users. Voice User Interface Projects teaches you how to develop voice-enabled applications for desktop, mobile, and Internet of Things (IoT) devices. This book explains in detail VUI and its importance, basic design principles of VUI, fundamentals of conversation, and the different voice-enabled applications available in the market. You will learn how to build your first voice-enabled application by utilizing DialogFlow and Alexa's natural language processing (NLP) platform. Once you are comfortable with building voice-enabled applications, you will understand how to dynamically process and respond to the questions by using NodeJS server deployed to the cloud. You will then move on to securing NodeJS RESTful API for DialogFlow and Alexa webhooks, creating unit tests and building voice-enabled podcasts for cars. Last but not the least you will discover advanced topics such as handling sessions, creating custom intents, and extending built-in intents in order to build conversational VUIs that will help engage the users. By the end of the book, you will have grasped a thorough knowledge of how to design and develop interactive VUIs. What you will learn Understand NLP platforms with machine learning Exploit best practices and user experiences in creating VUI Build voice-enabled chatbots Host, secure, and test in a cloud platform Create voice-enabled applications for personal digital assistant devices Develop a virtual assistant for cars Who this book is forVoice User Interface Projects is for you if you are a software engineer who wants to develop voice-enabled applications for your personal digital assistant devices such as Amazon Echo and Google Home, along with your car's virtual assistant systems. Some experience with JavaScript is required.

Get up and running with the fundamentals of Amazon Alexa and build exciting IoT projects Key Features Gain hands-on experience of working with Amazon Echo and Alexa Build exciting IoT projects using Amazon Echo Learn about voice-enabled smart devices Book DescriptionAmazon Echo is a smart speaker developed by Amazon, which connects to Amazon's Alexa Voice Service and is entirely controlled by voice commands. Amazon Echo is currently being used for a variety of purposes such as home automation, asking generic queries, and even ordering a cab or pizza. Alexa Skills Projects starts with a basic introduction to Amazon Alexa and Echo. You will then deep dive into Alexa Programming concepts such as Intents, Slots, Lambdas and maintaining your skill's state using DynamoDB. You will get a clear understanding of how some of the most popular Alexa Skills work, and gain experience of working with real-world Amazon Echo applications. In the concluding chapters, you will explore the future of voice-enabled applications and their coverage with respect to the Internet of Things. By the end of the book, you will have learned to design Alexa Skills for specific purposes and interact with Amazon Echo to execute these skills. What you will learn Understand how Amazon Echo is already being used in various domains Discover how an Alexa Skill is architected Get a clear understanding of how some of the most popular Alexa Skills work Design Alexa Skills for specific purposes and interact with Amazon Echo to execute them Gain experience of programming for Amazon Echo Explore future applications of Amazon Echo and other voice-activated devices Who this book is forAlexa Skills Projects is for individuals who want to have a deep understanding of the underlying technology that drives Amazon Echo and Alexa, and how it can be integrated with the Internet of Things to develop hands-on projects.

Spoken Dialogue Systems Technology and Design covers key topics in the field of spoken language dialogue interaction from a variety of leading researchers. It brings together several perspectives in the areas of corpus annotation and analysis, dialogue system construction, as well as theoretical perspectives on communicative intention, context-based generation, and modelling of discourse structure. These topics are all part of the general research and development within the area of discourse and dialogue with an emphasis on dialogue systems; corpora and corpus tools and semantic and pragmatic modelling of discourse and dialogue.

Dragon NaturallySpeaking For Dummies, 4E will introduce readers to everything they need to know to get started with this advanced voice recognition software. Readers will get the most up-to-date information on the latest version of the software. PART I: Hatching and Launching Your Dragon Software Chapter 1: Preparing for Dragons Chapter 2: Basic Training Chapter 3: Launching and Controlling Your Dragon PART II: Fire-Breathing 101 Chapter 4: Basic Dictating Chapter 5: Selecting, Editing, and Correcting in the NaturallySpeaking Window Chapter 6: Fonts, Alignment, and All That: Formatting Your Document Chapter 7: Proofreading and Listening to Your Text Chapter 8: Using Recorded Speech Chapter 9: Mobile Edition and NaturallyMobile Recorder PART III: Giving Your Applications Wings Chapter 10: Dictating into Other Applications Chapter 11: Controlling Your Desktop and Windows by Voice Chapter 12: Using NaturalWord for Word and WordPerfect Chapter 13: A Dragon Online Chapter 14: Dragon Your Data Around Chapter 15: Staying Organized on the Move PART IV: Precision Flying Chapter 16: Feeding Your Dragon: RAM, Disk Space, and Speed Chapter 17: Speaking More Clearly to Your Dragon Chapter 18: Additional Training and Vocabulary Building Chapter 19: Improving Audio Input Chapter 20: Dealing with Change Chapter 21: Having Multiple Users or Vocabularies Chapter 22: Creating Your Own Commands Chapter 23: Taking Draconian Measures: Workarounds for Problems PART V: The Part of Tens Chapter 24: Ten Common Problems Chapter 25: Ten Time-and-Sanity-Saving Tips Chapter 26: Ten Mistakes to Avoid Chapter 27: Ten Stupid Dragon Tricks

This book presents a systematic approach to the automatic recognition of simultaneous speech signals using computational auditory scene analysis. Inspired by human auditory perception, this book investigates a range of algorithms and techniques for decomposing multiple speech signals by integrating a spectro-temporal fragment decoder within a statistical search process. The outcome is a comprehensive insight into the mechanisms required if automatic speech recognition is to approach human levels of performance.

"Advances in Non-Linear Modeling for Speech Processing" includes advanced topics in non-linear estimation and modeling techniques along with their applications to speaker recognition.
Non-linear aeroacoustic modeling approach is used to estimate the important fine-structure speech events, which are not revealed by the short time Fourier transform (STFT). This aeroacostic modeling approach provides the impetus for the high resolution Teager energy operator (TEO). This operator is characterized by a time resolution that can track rapid signal energy changes within a glottal cycle.
The cepstral features like linear prediction cepstral coefficients (LPCC) and mel frequency cepstral coefficients (MFCC) are computed from the magnitude spectrum of the speech frame and the phase spectra is neglected. To overcome the problem of neglecting the phase spectra, the speech production system can be represented as an amplitude modulation-frequency modulation (AM-FM) model. To demodulate the speech signal, to estimation the amplitude envelope and instantaneous frequency components, the energy separation algorithm (ESA) and the Hilbert transform demodulation (HTD) algorithm are discussed.
Different features derived using above non-linear modeling techniques are used to develop a speaker identification system. Finally, it is shown that, the fusion of speech production and speech perception mechanisms can lead to a robust feature set.

Your Definitive Professional Resource

Develop real-world voice-based applications using this authoritative one-of-a-kind guide. Featuring in-depth coverage of both core and emerging topics within voice-enabled technology, this book explains everything from setting up a simple voice mail system to developing advanced multi-model voice applications using the newest Web telephony engine. You'll learn how to integrate VoiceXML with other key technologies such as ASP, JSP, ColdFusion, CCXML, and SALT. All examples are based on today's most current hardware. Containing project specifications, guidelines, deployment procedures--as well as actual case studies with all source code--this practical resource will change the way you develop next-generation voice-based applications.Design dialog flow and navigation architecture and learn guidelines for voice applications Manage content and identify target audience Learn VoiceXML document structure and execute multi-document-based applications Develop voice mail and voice banking systems using ASP and VoiceXML Identify the scope and role of grammars in VoiceXML 2.0 Use JSP to interact with databases and write code for front-end dialogs Understand the benefits and components of the Microsoft Web telephony engine Write CCXML programs and integrate CCXML with VoiceXML applications Produce speech output and speech input in SALT

Natural language processing (NLP) is a scientific discipline which is found at the interface of computer science, artificial intelligence and cognitive psychology. Providing an overview of international work in this interdisciplinary field, this book gives the reader a panoramic view of both early and current research in NLP. Carefully chosen multilingual examples present the state of the art of a mature field which is in a constant state of evolution. In four chapters, this book presents the fundamental concepts of phonetics and phonology and the two most important applications in the field of speech processing: recognition and synthesis. Also presented are the fundamental concepts of corpus linguistics and the basic concepts of morphology and its NLP applications such as stemming and part of speech tagging. The fundamental notions and the most important syntactic theories are presented, as well as the different approaches to syntactic parsing with reference to cognitive models, algorithms and computer applications.

Speech recognition in 'adverse conditions' has been a familiar area of research in computer science, engineering, and hearing sciences for several decades. In contrast, most psycholinguistic theories of speech recognition are built upon evidence gathered from tasks performed by healthy listeners on carefully recorded speech, in a quiet environment, and under conditions of undivided attention. Building upon the momentum initiated by the Psycholinguistic Approaches to Speech Recognition in Adverse Conditions workshop held in Bristol, UK, in 2010, the aim of this volume is to promote a multi-disciplinary, yet unified approach to the perceptual, cognitive, and neuro-physiological mechanisms underpinning the recognition of degraded speech, variable speech, speech experienced under cognitive load, and speech experienced by theoretically relevant populations. This collection opens with a review of the literature and a formal classification of adverse conditions. The research articles then highlight those adverse conditions with the greatest potential for constraining theory, showing that some speech phenomena often believed to be immutable can be affected by noise, surface variations, or attentional set in ways that will force researchers to rethink their theory. This volume is essential for those interested in speech recognition outside laboratory constraints.

"Advances in Speaker Recognition" presents a comprehensive analysis of the progress of speaker recognition. The material addresses the technical aspects of voice technology within the framework of societal needs, such as the use of speech recognition software to produce up-to-date electronic health records, not withstanding patients making changes to health plans and physicians. Due to global security concerns, there is a greater need to identify a person's identity from his or her voice. Included will be discussion of speaker biometrics literature, data collection, corpus design, the detection-to-error trade off curve, mono- and multi-lingual speaker detection, as well as research in mimic resistance.