Difference between revisions of "CMUSphinx- PocketSphinx"

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|Name=CMUSphinx- PocketSphinx
 
|Name=CMUSphinx- PocketSphinx
 
|Short description=a speaker-independent large vocabulary continuous speech recognizer for handheld and portable devices.
 
|Short description=a speaker-independent large vocabulary continuous speech recognizer for handheld and portable devices.
|Full description=Sphinx is a speaker-independent large vocabulary continuous speech recognizer. It is also a collection of free and open source tools and resources that allows researchers and developers to build speech recognition systems. The packages that the CMU Sphinx Group is releasing are a set of reasonably mature, world-class speech components that provide a basic level of technology to anyone interested in creating speech-using applications without the once-prohibitive initial investment cost in research and development; the same components are open to peer review by all researchers in the field, and are used for linguistic research as well. PocketSphinx is CMU's fastest speech recognition system. It uses Hidden Markov Models (HMM) with semi-continuous output probability density functions (PDF). Even though it is not as accurate as Sphinx-3 or Sphinx-4, it runs at real time, and therefore it is a good choice for live applications. You can find further documentation about PocketSphinx in the release documentation, or at the online documentation.
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|Full description=Sphinx is a speaker-independent large vocabulary continuous speech recognizer. It is also a collection of free software tools and resources that allows researchers and developers to build speech recognition systems. The packages that the CMU Sphinx Group is releasing are a set of reasonably mature, world-class speech components that provide a basic level of technology to anyone interested in creating speech-using applications without the once-prohibitive initial investment cost in research and development; the same components are open to peer review by all researchers in the field, and are used for linguistic research as well. PocketSphinx is CMU's fastest speech recognition system. It uses Hidden Markov Models (HMM) with semi-continuous output probability density functions (PDF). Even though it is not as accurate as Sphinx-3 or Sphinx-4, it runs at real time, and therefore it is a good choice for live applications. You can find further documentation about PocketSphinx in the release documentation, or at the online documentation.
 
|Homepage URL=http://www.speech.cs.cmu.edu/pocketsphinx/
 
|Homepage URL=http://www.speech.cs.cmu.edu/pocketsphinx/
 
|User level=advanced
 
|User level=advanced
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|Version status=planning
 
|Version status=planning
 
|Version download=https://sourceforge.net/projects/cmusphinx/files/pocketsphinx/5prealpha/pocketsphinx-5prealpha.tar.gz/download
 
|Version download=https://sourceforge.net/projects/cmusphinx/files/pocketsphinx/5prealpha/pocketsphinx-5prealpha.tar.gz/download
|Last review by=Alejandro Hernández Petermann
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|Last review by=Alejandroindependiente
 
|Last review date=2016/09/15
 
|Last review date=2016/09/15
 
|Submitted by=Database conversion
 
|Submitted by=Database conversion

Latest revision as of 15:23, 2 December 2022


[edit]

CMUSphinx- PocketSphinx

http://www.speech.cs.cmu.edu/pocketsphinx/
a speaker-independent large vocabulary continuous speech recognizer for handheld and portable devices.

Sphinx is a speaker-independent large vocabulary continuous speech recognizer. It is also a collection of free software tools and resources that allows researchers and developers to build speech recognition systems. The packages that the CMU Sphinx Group is releasing are a set of reasonably mature, world-class speech components that provide a basic level of technology to anyone interested in creating speech-using applications without the once-prohibitive initial investment cost in research and development; the same components are open to peer review by all researchers in the field, and are used for linguistic research as well. PocketSphinx is CMU's fastest speech recognition system. It uses Hidden Markov Models (HMM) with semi-continuous output probability density functions (PDF). Even though it is not as accurate as Sphinx-3 or Sphinx-4, it runs at real time, and therefore it is a good choice for live applications. You can find further documentation about PocketSphinx in the release documentation, or at the online documentation.





Licensing

License

Verified by

Verified on

Notes

License

X11

Verified by

Kelly Hopkins

Verified on

18 February 2009

Verified by

Kelly Hopkins

Verified on

18 February 2009




Leaders and contributors

Contact(s)Role
David Huggins-Daines Maintainer
Kevin Lenzo Maintainer
Evandro Gouvêa Maintainer
Alan W Black Maintainer
Mosur Ravishankar Maintainer


Resources and communication

AudienceResource typeURI
DeveloperDownloadhttp://cmusphinx.sourceforge.net/html/download.php#sphinxbase
DeveloperHomepagehttp://sourceforge.net/projects/cmusphinx
GeneralForumhttp://sourceforge.net/forum/forum.php?forum_id=5470
SupportForumhttp://sourceforge.net/forum/forum.php?forum_id=5471
Bug TrackingBug Trackinghttp://sourceforge.net/tracker/?group_id=1904&atid=101904


Software prerequisites




Entry









































Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.3 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. A copy of the license is included in the page “GNU Free Documentation License”.

The copyright and license notices on this page only apply to the text on this page. Any software or copyright-licenses or other similar notices described in this text has its own copyright notice and license, which can usually be found in the distribution or license text itself.