SMC/SoC/2008 - Revision history

Pravs: /* Guidelines for Students */

2008-03-18T16:07:32Z

Guidelines for Students

← Older revision		Revision as of 21:37, 18 March 2008
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	==Guidelines for Students ==		==Guidelines for Students ==
			[http://pradeepto.livejournal.com/12565.html How to write applications for KDE Google Summer Of Code?] - most of the tips applicable to all projects.

	==Guidelines for Mentors ==		==Guidelines for Mentors ==
	[http://www.gnome.org/~federico/docs/summer-of-code-mentoring-howto/index.html Summer of Code Mentoring HOWTO]		[http://www.gnome.org/~federico/docs/summer-of-code-mentoring-howto/index.html Summer of Code Mentoring HOWTO]

Missing actor at 04:09, 18 March 2008

2008-03-18T04:09:05Z

← Older revision		Revision as of 09:39, 18 March 2008
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	[[SMC/SoC/2007\|SMC in Google Summer of Code 2007]]		[[SMC/SoC/2007\|SMC in Google Summer of Code 2007]]

	~~[[User:Santhosh\|Santhosh Thottingal]] will be lead admin this year, and will deal with the administrative stuff with Google~~ for ~~SMC~~ to be ~~a mentoring organisation~~		SMC is not selected for GSOC 2008. Anyway these projects need to be done!

	==Ideas for Google Summer of Code 2008==		==Ideas for Google Summer of Code 2008==

117.196.131.49: /* Speech recognition system for Malayalam */

2008-03-16T02:54:42Z

Speech recognition system for Malayalam

← Older revision		Revision as of 08:24, 16 March 2008
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	===Speech recognition system for Malayalam===		===Speech recognition system for Malayalam===
	The aim is to develop a speech recognition system for Malayalam using the concepts of memory prediction framework. Memory prediction framework put forward by Jeff Hawkins in his book 'On Intelligence'(2004) is a theory of brain function, based on the hierarchical organization of human neocortex.It explains how the hierarchical structure enables brain to match sensory inputs to the stored memory patterns for predicting the future input sequences. According to this model, neocortex is a layered structure with different layers storing constructs of varying complexity, with sensory inputs coming to the lowest layer. For example in case of vision, the lower layer receives retinal signals and layers up the hierarchy associates themselves with meaningful constructs like lines, two dimensional figures, and furthur up specific objects like faces etc. In speech the layers store different speech constructs from phonemes and syllables to phrases and sentences. The human speech perception and recognition can be understood using this hierarchical organization.		The aim is to develop a speech recognition system for Malayalam using the concepts of memory prediction framework. Memory prediction framework put forward by Jeff Hawkins in his book 'On Intelligence'(2004) is a theory of brain function, based on the hierarchical organization of human neocortex.It explains how the hierarchical structure enables brain to match sensory inputs to the stored memory patterns for predicting the future input sequences. According to this model, neocortex has a layered structure with different layers storing constructs of varying complexity, with sensory inputs coming to the lowest layer. For example in case of vision, the lower layer receives retinal signals and layers up the hierarchy associates themselves with meaningful constructs like lines, two dimensional figures, and furthur up specific objects like faces etc. In speech the layers store different speech constructs from phonemes and syllables to phrases and sentences. The human speech perception and recognition can be understood using this hierarchical organization.
	If we mimic the way in which human brain recognizes speech, the resulting system will be more robust than the existing systems. The proposed system is trained with a carefully compiled database and different speech constructs are stored in different layers.When a speech segment to be recognized is given, a series of predictions start and signals will be passed upwards and downwards the layers, until the most probable speech construct is arrived at. For example if the most probable candidate for first word is 'how', predictions start as to what succeeding words can be. This continues until the last word is arrived at and the phrase giving maximum probability will chosen among these predictions.		If we mimic the way in which human brain recognizes speech, the resulting system will be more robust than the existing systems. The proposed system is trained with a carefully compiled database and different speech constructs are stored in different layers.When a speech segment to be recognized is given, a series of predictions start and signals will be passed upwards and downwards the layers, until the most probable speech construct is arrived at. For example if the most probable candidate for first word is 'how', predictions start as to what succeeding words can be. This continues until the last word is arrived at and the phrase giving maximum probability will chosen among these predictions.

117.196.131.49: /* Speech recognition system for Malayalam */

2008-03-16T02:50:55Z

Speech recognition system for Malayalam

← Older revision		Revision as of 08:20, 16 March 2008
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	===Speech recognition system for Malayalam===		===Speech recognition system for Malayalam===
	The aim is to develop a speech recognition system for Malayalam using the concepts of memory prediction framework. ~~Meory~~ prediction framework put forward by Jeff Hawkins in his book 'On Intelligence'(2004) is a theory of brain function, based on the hierarchical organization of human neocortex.It explains how ~~this~~ hierarchical structure enables brain to match sensory inputs to the stored memory patterns for predicting the future input sequences. According to this model, neocortex as a layered structure with different layers storing constructs of varying complexity, with sensory inputs coming to the lowest layer. For example in case of vision, the lower layer receives retinal signals and layers up the hierarchy associates themselves with meaningful constructs like lines, two dimensional figures, and furthur up specific objects like faces etc. In speech the layers store different speech constructs from phonemes and syllables to phrases and sentences. The human speech perception and recognition can be understood using this hierarchical organization ~~and the ability of brain to predict the future inputs~~.		The aim is to develop a speech recognition system for Malayalam using the concepts of memory prediction framework. Memory prediction framework put forward by Jeff Hawkins in his book 'On Intelligence'(2004) is a theory of brain function, based on the hierarchical organization of human neocortex.It explains how the hierarchical structure enables brain to match sensory inputs to the stored memory patterns for predicting the future input sequences. According to this model, neocortex is a layered structure with different layers storing constructs of varying complexity, with sensory inputs coming to the lowest layer. For example in case of vision, the lower layer receives retinal signals and layers up the hierarchy associates themselves with meaningful constructs like lines, two dimensional figures, and furthur up specific objects like faces etc. In speech the layers store different speech constructs from phonemes and syllables to phrases and sentences. The human speech perception and recognition can be understood using this hierarchical organization.
	If we mimic the way in which human brain recognizes speech, the resulting system will be more robust than existing systems. The system is ~~first~~ trained with a carefully compiled database and different speech constructs are stored in different layers.When a speech segment to be recognized is given, a series of predictions start and signals will be passed upwards and downwards the layers, until the most probable speech construct is arrived at. For example if the most probable candidate for first word is 'how', predictions start as to what succeeding words can be. This continues until the last word and the phrase giving maximum ~~most~~ probability will chosen among these predictions.		If we mimic the way in which human brain recognizes speech, the resulting system will be more robust than the existing systems. The proposed system is trained with a carefully compiled database and different speech constructs are stored in different layers.When a speech segment to be recognized is given, a series of predictions start and signals will be passed upwards and downwards the layers, until the most probable speech construct is arrived at. For example if the most probable candidate for first word is 'how', predictions start as to what succeeding words can be. This continues until the last word is arrived at and the phrase giving maximum probability will chosen among these predictions.

	References:		References:

117.196.131.49: /* Speech recognition system for Malayalam */

2008-03-16T02:40:46Z

Speech recognition system for Malayalam

← Older revision		Revision as of 08:10, 16 March 2008
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	===Speech recognition system for Malayalam===		===Speech recognition system for Malayalam===
	~~Develop~~ a speech recognition system for Malayalam using the concepts of memory prediction framework		The aim is to develop a speech recognition system for Malayalam using the concepts of memory prediction framework. Meory prediction framework put forward by Jeff Hawkins in his book 'On Intelligence'(2004) is a theory of brain function, based on the hierarchical organization of human neocortex.It explains how this hierarchical structure enables brain to match sensory inputs to the stored memory patterns for predicting the future input sequences. According to this model, neocortex as a layered structure with different layers storing constructs of varying complexity, with sensory inputs coming to the lowest layer. For example in case of vision, the lower layer receives retinal signals and layers up the hierarchy associates themselves with meaningful constructs like lines, two dimensional figures, and furthur up specific objects like faces etc. In speech the layers store different speech constructs from phonemes and syllables to phrases and sentences. The human speech perception and recognition can be understood using this hierarchical organization and the ability of brain to predict the future inputs.
			If we mimic the way in which human brain recognizes speech, the resulting system will be more robust than existing systems. The system is first trained with a carefully compiled database and different speech constructs are stored in different layers.When a speech segment to be recognized is given, a series of predictions start and signals will be passed upwards and downwards the layers, until the most probable speech construct is arrived at. For example if the most probable candidate for first word is 'how', predictions start as to what succeeding words can be. This continues until the last word and the phrase giving maximum most probability will chosen among these predictions.

			References:
			#"On Intelligence", Jeff Hawkins, Sandra Blakeslee; Henry Holt, 2004
			#"Hierarchical Temporal Memory - Concepts, Theory, and Terminology" by Jeff Hawkins and Dileep George, Numenta Inc.
			#http://www.phillylac.org/prediction

	===Creating a new family of Equal Height Fonts (EHF)for Malayalam language===		===Creating a new family of Equal Height Fonts (EHF)for Malayalam language===
	To design and create a new family of Equal Height Fonts for the traditional Malayalam		To design and create a new family of Equal Height Fonts for the traditional Malayalam

Missing actor: /* Rewrite the Dhvani sound system with SDL */

2008-03-14T08:18:10Z

Rewrite the Dhvani sound system with SDL

← Older revision		Revision as of 13:48, 14 March 2008
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	The goal of [http://www.freebsoft.org/speechd Speech Dispatcher] project is to provide a high-level device independent layer for speech synthesis through a simple, stable and well documented interface. Since SD is more discussed to act as a unified TTS layer for both gnome and KDE, We can try to write a Interface for that		The goal of [http://www.freebsoft.org/speechd Speech Dispatcher] project is to provide a high-level device independent layer for speech synthesis through a simple, stable and well documented interface. Since SD is more discussed to act as a unified TTS layer for both gnome and KDE, We can try to write a Interface for that

	===Rewrite the Dhvani sound system with SDL===		===Rewrite the Dhvani sound system with SDL and Additional APIs===
	#Rewrite the ALSA sound system of [[Dhvani\|Dhvani]] with [http://www.libsdl.org/ SDL] to make it a cross platform application		#Rewrite the ALSA sound system of [[Dhvani\|Dhvani]] with [http://www.libsdl.org/ SDL] to make it a cross platform application
	#Packaging for different platforms		#Packaging for different platforms
	#Bug fixes for langauge modules and Code clean up		#Bug fixes for langauge modules and Code clean up
	#Adding pitch/volume/pause support for the generated speech		#Adding pitch/volume/pause support for the generated speech
			#API to stop the speech in between a synthesis
			#Provide Dhvani as a library
			#API to check whether the synthesizer is producing speech(isSpeaking)

	===Localization of Free Content Management Systems to Malayalam-Drupal &Joomla ===		===Localization of Free Content Management Systems to Malayalam-Drupal &Joomla ===

Missing actor: /* Functional Optical character Recognition system */

2008-03-14T08:10:30Z

Functional Optical character Recognition system

← Older revision		Revision as of 13:40, 14 March 2008
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	http://code.google.com/p/tesseract-ocr/		http://code.google.com/p/tesseract-ocr/

	http://code.google.com/p/ocropus/		http://code.google.com/p/ocropus/

Missing actor: /* Write a Gnome Speech Driver for Dhvani and Integrate it with Orca */

2008-03-14T08:09:56Z

Write a Gnome Speech Driver for Dhvani and Integrate it with Orca

← Older revision		Revision as of 13:39, 14 March 2008
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	#Orca for visually impaired users uses gnome speech for speech engines. Currently Festival, Espeak, freetts etc have drivers for gnome speech. We need to write a driver for dhvani.		#Orca for visually impaired users uses gnome speech for speech engines. Currently Festival, Espeak, freetts etc have drivers for gnome speech. We need to write a driver for dhvani.
	#Develop plugins for KTTS/Gedit/Firefox		#Develop plugins for KTTS/Gedit/Firefox
			=== Write a Dhvani Interface for Speech Dispatcher ===
			The goal of [http://www.freebsoft.org/speechd Speech Dispatcher] project is to provide a high-level device independent layer for speech synthesis through a simple, stable and well documented interface. Since SD is more discussed to act as a unified TTS layer for both gnome and KDE, We can try to write a Interface for that

	===Rewrite the Dhvani sound system with SDL===		===Rewrite the Dhvani sound system with SDL===

Missing actor: /* Speech recognition system for Malayalam */

2008-03-13T06:32:11Z

Speech recognition system for Malayalam

← Older revision		Revision as of 12:02, 13 March 2008
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	===Speech recognition system for Malayalam===		===Speech recognition system for Malayalam===
	Develop a speech recognition system for Malayalam using the concepts of memory prediction framework		Develop a speech recognition system for Malayalam using the concepts of memory prediction framework
			===Creating a new family of Equal Height Fonts (EHF)for Malayalam language===
			To design and create a new family of Equal Height Fonts for the traditional Malayalam
			script. Following Roman typology, serif and sans serif type of font variations are available in
			Malayalam. Equal Width Fonts, such as Courier, available in Roman typography are
			impossible for Malayalam characters and this is unnecessary. The proposed Equal Height
			Fonts is a new concept in the history of font making to surmount the typographical
			challenge of vertically stacked conjuncts.

	==How to Apply ==		==How to Apply ==

202.83.35.108: /* Ideas for Google Summer of Code 2008 */

2008-03-12T13:53:21Z

Ideas for Google Summer of Code 2008

← Older revision		Revision as of 19:23, 12 March 2008
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	===Speech recognition system for Malayalam===		===Speech recognition system for Malayalam===
	#Develop a speech recognition system for Malayalam using the concepts of memory prediction framework		Develop a speech recognition system for Malayalam using the concepts of memory prediction framework

	==How to Apply ==		==How to Apply ==