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

Pluralsight - Using the Speech Recognition and Synthesis .NET APIs  eBooks & eLearning

Posted by naag at March 4, 2016
Pluralsight - Using the Speech Recognition and Synthesis .NET APIs

Pluralsight - Using the Speech Recognition and Synthesis .NET APIs
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 3h 16m | 604 MB
Genre: eLearning | Language: English

This is an introductory course on how to utilize the speech recognition and synthesis APIs in the .NET framework.

"Modern Speech Recognition Approaches with Case Studies" ed. by S. Ramakrishnan  eBooks & eLearning

Posted by exLib at Feb. 23, 2016
"Modern Speech Recognition Approaches with Case Studies" ed. by S. Ramakrishnan

"Modern Speech Recognition Approaches with Case Studies" ed. by S. Ramakrishnan
ITAe | 2012 | ISBN: 9789535108313 | 337 pages | PDF | 12 MB

This book focuses primarily on speech recognition and the related tasks such as speech enhancement and modeling.
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
Application of Hidden Markov Models in Speech Recognition

Mark Gales, Steve Young, "Application of Hidden Markov Models in Speech Recognition"
English | 2008 | ISBN: 1601981201 | PDF | pages: 124 | 1,6 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.
Statistical Methods for Speech Recognition (Language, Speech, and Communication) by Frederick Jelinek

Statistical Methods for Speech Recognition (Language, Speech, and Communication) by Frederick Jelinek
English | Jan. 16, 1998 | ISBN: 0262100665 | 305 Pages | DJVU | 2 MB

This book reflects decades of important research on the mathematical foundations of speech recognition. It focuses on underlying statistical techniques such as hidden Markov models, decision trees, the expectation-maximization algorithm…
Visual Speech Recognition: Lip Segmentation and Mapping (Repost)

Alan Wee-Chung Liew, Shilin Wang, "Visual Speech Recognition: Lip Segmentation and Mapping"
English | 2008 | ISBN: 1605661864 | PDF | pages: 573 | 123 mb
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.