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===Problem Statement===
Malayalam is one among 22 languages spoken in India with about 38 million speakers. Development of malayalam speech recognition system is in its infancy stage; although many works have been done in other Indian languages. To develop an automatic continuous speech recognition system for a language , an acoustic model and language Model has to be developed for that particular language. At present acoustic and language models , for continuous speech recognition , are not available for Malayalam Language . 
===Synopsis===
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 an a high-quality acoustic model and language model for Malayalam with reasonable WER ( Word Error Rate )malayalam
The entire project can be subdivided in four parts :
:*Building language model
:::A language model gives the probabilities of sequences of words. Here for continuous speech speech recognition we use statistical modelling of language using CMUCLMTK. Estimating the probability of sequences can become difficult in corpora, in which phrases or sentences can be arbitrarily long and hence some sequences are not observed during training of the language model (data sparseness problem of over fittingoverfitting). Hence forming a good quality language model is a challenge.  '''Examples of language specific challenges''' Malayalam has 37 consonants and 16 vowels in the language. It is a syllable based language and written with syllabic alphabet in which all consonants have an inherent vowel /a/. There are different spoken forms in Malayalam although the literary dialect throughout Kerala is almost uniform. :*People have a hard time pronouncing breathy-voiced plosives and tends to substitute them with voiceless aspirated ones in the same place of articulation. :*ഫ ( ph’a ) pronounced differently in ഫലം and ഫാന്‍ . ന (na) (Nasal dental and Nasal alveolar) is pronounced differently even though the grapheme notation is same (eg. നനക്കുക (nan’naykkuka). phonological rules have been applied manually and edited the dictionary :*In continuous speech, word boundaries are also challenging. For instance, the word "thalasthanam” (തലസ്ഥാനം ) can be misconstrued as "thala sthanam” (തല സ്ഥാനം ). :*Articulation of certain phonemes are context dependent .For eg: the words ബലം and ജലം are pronounced as ബെലം and ജെലം respectively . :*The prosody of spoken Malayalam makes it difficult to correctly identify the sound units.(Phonemes). In order to address these language specific issues of Malayalam speech recognition we need to have a working acoustic model and language model, which is unfortunately not available or in naive state for Malayalam language. Our aim is to develop a working acoustic and language model and thereafter address language specific issues one by one as possible in the limited time constraint.
===Benefits===
# Language data is the key ingredient in terms of research and development in the area of language technology. The data ( speech corpora and text corpora ) collected for this project will be made publicly available for public for future works .# High quality acoustic model and language model for Malayalam malayalam with low WER(word error rate) will be developed which can be used for research and development purposes in Malayalam Speech Recognition and Processing area .
# Acoustic and Language model developed can be used by programmers/developers directly to create solutions to many existing problems that need speech recognition in local language.
# Understanding CMU Sphinx Engine and its tools to make language specific improvements and increase efficiency
# Finding the appropriate language specific parameter values and configuring the trainer
 
===References===
 
#[http://cmusphinx.sourceforge.net/wiki/‎ CMU Sphinx Website]
#[http://www.speech.cs.cmu.edu/sphinx/tutorial.html Learning to use the CMU SPHINX Automatic Speech Recognition system ]
#[http://cmusphinx.sourceforge.net/wiki/tutoriallm Building Language Model]
#[http://www.cambridge.org/gb/knowledge/isbn/item1150358/?site_locale=en_GB Introducing Speech and Language Processing - John Coleman]
==Experience==
===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. 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.
===July 1st - July 15th===
 Configuring the trainer parameters such as number of states in HMM(Hidden Markov Chain), different number of gaussian mixtures and tied states based on language specific features. Stressed consonants need to be treated separately and not as variants of the parent consonant for acoustic modeling . Finally training Training the initial acoustic model and building the language model.
===July 16th - July 28th===
Handling any unexpected issues regarding the data collected and finally retrainingOnce a working acoustic and language model has been formed further language specific improvements can be performed . Consulting linguists for incorporating Malayalam grammar rules to improve the recognition accuracy of the speech recognition system is one such method models.
===Midterm Evaluation===
 
Mid-Term should provide the community with a reasonably good acoustic model and language model for Malayalam.
===August===
 This time will be utilized Applying optimisations including graphemes to address phoneme conversion and solve as many optimal text selection algorithms for text corpora . Choosing appropriate speakers based on data statistics is also done during this period. Finally training of the language specific problems mentioned in the project proposal . Multiple Forced alignment iterations could be done optimised data to further improve form the high quality acoustic model and language model.
===September 1st- September 15th===
 
Can be used for general bug fixing and detailed documentation.
===Final Evaluation===
 Acoustic Expects to complete a high quality acoustic model and language model for malayalam with low WER(word error rate) will be formed.
===Pens Down===
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