Continuous Density Hidden Markov Model for Hindi Speech Recognition

Abstract

State of the art automatic speech recognitionsystem uses Mel frequency cepstral coefficients as featureextractor along with Gaussian mixture model for acousticmodeling but there is no standard value to assign number ofmixture component in speech recognition process.Currentchoice of mixture component is arbitrary with littlejustification. Also the standard set for European languagescan not be used in Hindi speech recognition due to mismatchin database size of the languages.Parameter estimation withtoo many or few component may inappropriately estimatethe mixture model. Therefore, number of mixture isimportant for initial estimation of expectation maximizationprocess. In this research work, the authors estimate numberof Gaussian mixture component for Hindi database basedupon the size of vocabulary.Mel frequency cepstral featureand perceptual linear predictive feature along with itsextended variations with delta-delta-delta feature have beenused to evaluate this number based on optimal recognitionscore of the system . Comparitive analysis of recognitionperformance for both the feature extraction methods onmedium size Hindi database is also presented in thispaper.HLDA has been used as feature reduction techniqueand also its impact on the recognition score has beenhighlighted