Continuous density Hidden Markov Model for context dependent Hindi speech recognition

With the advancement in technology and the inherent advantage of voice based communication due to its variability, speed and convenience has driven attention towards mechanical recognition of speech. Literature survey of research in this area shows that almost every system uses Gaussian Mixture Hidden Markov model for optimal performance in recognition of speech. Evaluation of Gaussian likelihood dominates the total computational load in using this statistical approach. The appropriate selection of Gaussian mixture is very important. Current choice of mixture component is arbitrary with little justification. Also the standard set for European languages can not be used in Hindi speech recognition due to mismatch in database size of the languages. Parameter estimation with too many or few component may inappropriately estimate the mixture model. Therefore, number of mixture is important for expectation maximization process. In this research work, the authors estimate number of Gaussian mixture component for Hindi database based upon the size of vocabulary. MFCC and PLP features along with its extended version has been used as speech feature. HLDA is applied for feature reduction while using extended features.