== Personal Information ==
#Email Address : 2ar.rahul@gmail. com #Telephone : +919446048820 # University and Education : BTech in Computer Science , College of Engineering Trivandrum ( University of Kerala )
=== Why do you want to work with the Swathanthra Malayalam Computing ? ===
I think most of the technological advancements in the field of computer science is inaccessible to the majority of general public due to lack of local language support .SMC, with its slogan "എന്റെ കമ്പ്യൂട്ടറിനു് എന്റെ ഭാഷ " (my language for my computer), has always been in the forefront for the same.Malyalam being my mother tongue i believe i can contribute to the SMC community.
=== Do you have any past involvement with the Swathanthra Malayalam Computing or another open source project as a contributor? ===
== Proposal Description ==
The project aims at building an Acoustic model and Language Model for Malayalam language , which will be very useful for research and development purposes in Malayalam Speech Recognition and Processing area .
CMU Sphinx is an open source toolkit for speech recognition developed by carnegie mellon university.It contains series of speech recognizers of which latest is sphinx4 , acoustic model trainer (sphinx train) and a statsitical language model builder (cmuclmtk). For developing a continous speech recognition system we need well trained acoustic model and language model.An acousitc model process audio recordings with their transcriptions and form statstical representations of word. A language model describes the likelihood, probability, or penalty taken when a sequence or collection of words is seen.
CMUSphinx project comes with several high-quality acoustic models and language model for language like english, french, spanish etc.
The aim of this project as a whole is to develop a high-quality acoustic model and language model for malayalam.
The initial goal of the project is creating the database required which involves : ::* Collecting voice data and making transcription for acoustic model ::*Collecting text corpora for language model
Once the database is formed we can start training the acoustic model using sphinxtrain and build language model using cmuclmtk . Although we have not applied any optimisation at this stage of the project we will have successfully created a working acoustic and language model.
Optimisations , careful selection of voice and text data that can better represent the language , can be performed at this stage/phase so that quality of the acoustic model and language model created can be improved.
Grapheme to phoneme converters and optimal text selection algorithm can be used to select a set phonetically rich sentences from a huge text corpus.
Appropriate speaker selection and using data statistics can greatly improve the quality of collected acoustic data.
#Languages : C,C++,Python,Java,Bash
#Embedded Platforms : Arduino , Atmel AVR , SiliconLabs CIP-
I am passionate about technology and free and open source systems. I am actively involved in the free software community and have volunteered for various free/open projects in the past . I have participated in various national level conferences ( like FOSS.IN , Pycon India ) that promote FOSS.
*Intern as Linux Distribution Developer [ Winter 2010 ]
===Unavailable - May 6th to June 2nd===
University tests and other academic responsibilities .
===June 2nd - June 15th===
I am familiar with the usage of sphinxtrain and cmuclmtk so i will be using this time to understand and learn to configure the internal parameters of the sphinx engine to improve performance of models formed.
===June 15th - June 30===
During this period i will be collecting all the voice data and text corpora required for the acoustic model and language model respectively.
===July 1st - July 15th===
Training the initial acoustic model and building the language model .
===July 16th - July 28th===
Handling any unexpected issues regarding the data collected and finally retrainingthe models.
Mid-Term should provide the community with a reasonably good acoustic model and language model for Malayalam.
Applying optimisations including graphemes to phoneme conversion and optimal text selection algorithms for text corpora . Choosing appropriate speakers based on data statistics is also done during this period. Finally training of the optimised data to form the high quality acoustic model and language model.
===September 1st- September 15th===
Can be used for general bug fixing and detailed documentation.
Expects to complete a high quality acoustic model and language model for malayalam with low WER(word error rate).