Speech Recognition

Speech Recognition Using Articulatory and Excitation Source Features (SpringerBriefs in Electrical and Computer Engineering)

Speech Recognition Using Articulatory and Excitation Source Features (SpringerBriefs in Electrical and Computer Engineering) by K. Sreenivasa Rao
English | 2017 | ISBN: 3319492195 | 92 Pages | PDF | 2.49 MB

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

"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.

Speech Recognition Over Digital Channels: Robustness and Standards (Repost)  eBooks & eLearning

Posted by Specialselection at Dec. 24, 2013
Speech Recognition Over Digital Channels: Robustness and Standards (Repost)

Antonio Peinado, Jose Segura, "Speech Recognition Over Digital Channels: Robustness and Standards"
English | 2006-09-11 | ISBN: 0470024003 | 274 pages | PDF | 2.8 mb

Robust Speech Recognition of Uncertain or Missing Data: Theory and Applications  eBooks & eLearning

Posted by tot167 at July 20, 2011
Robust Speech Recognition of Uncertain or Missing Data: Theory and Applications

Dorothea Kolossa, Reinhold Häb-Umbach, "Robust Speech Recognition of Uncertain or Missing Data: Theory and Applications"
Sp in ger | 2011 | ISBN: 3642213162 | 394 pages | PDF | 4,5 MB

Automatic Speech Recognition on Mobile Devices and over Communication Networks  eBooks & eLearning

Posted by step778 at Feb. 10, 2017
Automatic Speech Recognition on Mobile Devices and over Communication Networks

Zheng-Hua Tan, Boerge Lindberg, "Automatic Speech Recognition on Mobile Devices and over Communication Networks"
2008 | pages: 404 | ISBN: 1848001428 | PDF | 3,6 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.

Techniques for Noise Robustness in Automatic Speech Recognition (Repost)  eBooks & eLearning

Posted by step778 at Feb. 20, 2016
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  eBooks & eLearning

Posted by DZ123 at Oct. 25, 2015
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)  eBooks & eLearning

Posted by MoneyRich at Oct. 11, 2015
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.