[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-129278-en":3,"doc-seo-129278-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},129278,962085662650,"Jiven","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",6,"Technology","Automatic Speech Recognition - A Deep Learning Approach - Foreword & Preface","Automatic Speech Recognition (ASR) focuses on enabling natural human–machine interaction, building on decades of core methods such as GMMs, HMMs, MFCCs, n-gram language models, discriminative training, and adaptation techniques. This work positions deep learning as a central driver of the 4th generation ASR, emphasizing both DNN technologies and the expansion toward deep generative models. It outlines rigorous mathematical background, software implementation details, and comparisons between recurrent neural nets and hidden dynamic models to guide new system development and deepen expertise.","Signals and Communication Technology  \nDong Yu Li Deng  \nAutomatic Speech Recognition  \nA Deep Learning Approach  \nSignals and Communication Technology  \nMore information about this series at [http://www.springer.com/series/4748](http://www.springer.com/series/4748)  \nDong Yu • Li Deng  \nAutomatic Speech Recognition  \nA Deep Learning Approach  \n1 3  \nDong Yu Microsoft Research Bothell  \nUSA  \nLi Deng Microsoft Research Redmond, WA USA  \nISSN 1860-4862  \nISBN 978-1-4471-5778-6  \nISSN 1860-4870 (electronic) ISBN 978-1-4471-5779-3 (eBook)  \nDOI 10.1007/978-1-4471-5779-3  \nLibrary of Congress Control Number: 2014951663 Springer London Heidelberg New York Dordrecht  \n© Springer-Verlag London 2015  \nThis work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violations are liable to prosecution under the respective Copyright Law.  \nThe use of general descriptive names, registered names, trademarks, service marks, [etc. in](etc. in) this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.  \nWhile the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein.  \nPrinted on acid-free paper  \nSpringer is part of Springer Science+Business Media ([www.springer.com](www.springer.com))  \nTo my wife and parents  \nDong Yu  \nTo Lih-Yuan, Lloyd, Craig, Lyle, Arie, and Axel  \nLi Deng  \nForeword  \nThis is the first book on automatic speech recognition (ASR) that is focused on the deep learning approach, and in particular, deep neural network (DNN) technology. The landmark book represents a big milestone in the journey of the DNN technology, which has achieved overwhelming successes in ASR over the past few years. Following the authors ’ recent book on “Deep Learning: Methods and Applications”, this new book digs deeply and exclusively into ASR technology and applications, which were only relatively lightly covered in the previous book in parallel with numerous other applications of deep learning. Importantly, the background material of ASR and technical detail of DNNs including rigorous mathematical descriptions and software implementation are provided in this book, invaluable for ASR experts as well as advanced students.  \nOne unique aspect of this book is to broaden the view of deep learning from DNNs, as commonly adopted in ASR by now, to encompass also deep generative models that have advantages of naturally embedding domain knowledge and problem constraints. The background material did justice to the incredible richness of deep and dynamic generative models of speech developed by ASR researchers since early 90’s, yet without losing sight of the unifying principles with respect to the recent","cbCairB5N2rMYEFd","https://ap.wps.com/l/cbCairB5N2rMYEFd","pdf",6763378,3,1,329,"English","en",105,"# Foreword\n# Preface","[{\"question\":\"What is the main focus of this ASR book?\",\"answer\":\"The book concentrates on Automatic Speech Recognition using a deep learning approach, with emphasis on deep neural networks and their role in modern ASR.\"},{\"question\":\"Which historical ASR technologies are highlighted as foundational?\",\"answer\":\"It reviews classic ASR components including GMMs, HMMs, MFCCs and derivatives, n-gram language models, discriminative training, and adaptation techniques.\"},{\"question\":\"How does the book extend beyond DNNs in ASR?\",\"answer\":\"It broadens deep learning from primarily discriminative DNNs to include deep generative models, describing how generative modeling can embed domain knowledge and constraints.\"}]","Automatic Speech Recognition - A Deep Learning Approach - Foreword & Preface | PDF",1786104453,829,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"automatic-speech-recognition-a-deep-learning-approach-foreword-preface","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/technology/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/automatic-speech-recognition-a-deep-learning-approach-foreword-preface/129278/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-28","2026-08-07",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the main focus of this ASR book?","Question",{"text":76,"@type":77},"The book concentrates on Automatic Speech Recognition using a deep learning approach, with emphasis on deep neural networks and their role in modern ASR.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which historical ASR technologies are highlighted as foundational?",{"text":81,"@type":77},"It reviews classic ASR components including GMMs, HMMs, MFCCs and derivatives, n-gram language models, discriminative training, and adaptation techniques.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the book extend beyond DNNs in ASR?",{"text":85,"@type":77},"It broadens deep learning from primarily discriminative DNNs to include deep generative models, describing how generative modeling can embed domain knowledge and constraints.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,114,119,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":112,"slug":113},50,"technology",{"id":115,"doc_module":4,"doc_module_name":47,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":120,"doc_module":4,"doc_module_name":47,"category_name":121,"show_sort_weight":122,"slug":123},8,"Research & Report",30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]