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 .
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 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 overfitting). Hence forming a good quality language model is a challenge .
# 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 available
for public for future works# High quality acoustic model and language model for 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
===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).