Automatic Speech Recognition

Language Modeling for Automatic Speech Recognition of Inflective Languages  eBooks & eLearning

Posted by arundhati at Oct. 22, 2016
Language Modeling for Automatic Speech Recognition of Inflective Languages

Gregor Donaj, "Language Modeling for Automatic Speech Recognition of Inflective Languages: An Applications-Oriented Approach Using Lexical Data"
2016 | ISBN-10: 3319416057 | 80 pages | PDF | 1 MB
Techniques for Noise Robustness in Automatic Speech Recognition (Repost)

Tuomas Virtanen, Rita Singh, Bhiksha Raj, "Techniques for Noise Robustness in Automatic Speech Recognition"
2012 | pages: 500 | ISBN: 1119970881 | PDF | 8,6 mb
Acoustical and Environmental Robustness in Automatic Speech Recognition (repost)

Acoustical and Environmental Robustness in Automatic Speech Recognition by A. Acero
English | 13 July 2013 | ISBN: 1461363667 | 212 Pages | PDF | 14 MB

The need for automatic speech recognition systems to be robust with respect to changes in their acoustical environment has become more widely appreciated in recent years, as more systems are finding their way into practical applications. Although the issue of environmental robustness has received only a small fraction of the attention devoted to speaker independence, even speech recognition systems that are designed to be speaker independent frequently perform very poorly when they are tested using a different type of microphone or acoustical environment from the one with which they were trained.
Automatic Speech Recognition: A Deep Learning Approach (repost)

Automatic Speech Recognition: A Deep Learning Approach (Signals and Communication Technology) by Dong Yu and Li Deng
English | 2014 | ISBN: 1447157788 | 321 pages | PDF | 7,5 MB
Acoustical and Environmental Robustness in Automatic Speech Recognition

Acoustical and Environmental Robustness in Automatic Speech Recognition (The Springer International Series in Engineering and Computer Science) by Alex Acero
English | 1993 | ISBN: 1461363667 | 186 Pages | PDF | 14 MB

The need for automatic speech recognition systems to be robust with respect to changes in their acoustical environment has become more widely appreciated in recent years, as more systems are finding their way into practical applications. Although the issue of environmental robustness has received only a small fraction of the attention devoted to speaker independence, even speech recognition systems that are designed to be speaker independent frequently perform very poorly when they are tested using a different type of microphone or acoustical environment from the one with which they were trained.

Automatic Speech Recognition: A Deep Learning Approach  

Posted by interes at Feb. 17, 2015
Automatic Speech Recognition: A Deep Learning Approach

Automatic Speech Recognition: A Deep Learning Approach (Signals and Communication Technology) by Dong Yu and Li Deng
English | 2014 | ISBN: 1447157788 | 321 pages | PDF | 7,5 MB
Automatic Speech Recognition on Mobile Devices and over Communication Networks

Automatic Speech Recognition on Mobile Devices and over Communication Networks (Advances in Computer Vision and Pattern Recognition) by Zheng-Hua Tan
Springer; 2008 edition | March 3, 2008 | English | ISBN: 0387950486 | 402 pages | PDF | 3 MB

The advances in computing and networking have sparked an enormous interest in deploying automatic speech recognition on mobile devices and over communication networks. This book brings together academic researchers and industrial practitioners to address the issues in this emerging realm and presents the reader with a comprehensive introduction to the subject of speech recognition in devices and networks.
Techniques for Noise Robustness in Automatic Speech Recognition

Techniques for Noise Robustness in Automatic Speech Recognition By Tuomas Virtanen, Rita Singh, Bhiksha Raj
2012 | 514 Pages | ISBN: 1119970881 | PDF | 9 MB
Techniques for Noise Robustness in Automatic Speech Recognition

Tuomas Virtanen, "Techniques for Noise Robustness in Automatic Speech Recognition"
Publisher: Wiley | 2012 | ISBN: 1119970881 | 500 pages | PDF | 8.7 MB

Speech Recognition  

Posted by Saunt at July 5, 2009
Speech Recognition

Speech Recognition
Publisher: IN-TECH | ISBN: 9537619299 | edition 2008 | PDF | 576 pages | 35,3 mb

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes.