Continuous Density Hidden Markov Model for Hindi Speech Recognition

Abstract

State of the art automatic speech recognition
system uses Mel frequency cepstral coefficients as feature
extractor along with Gaussian mixture model for acoustic
modeling but there is no standard value to assign number of
mixture component in speech recognition process.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 initial estimation of expectation maximization
process. In this research work, the authors estimate number
of Gaussian mixture component for Hindi database based
upon the size of vocabulary.Mel frequency cepstral feature
and perceptual linear predictive feature along with its
extended variations with delta-delta-delta feature have been
used to evaluate this number based on optimal recognition
score of the system . Comparitive analysis of recognition
performance for both the feature extraction methods on
medium size Hindi database is also presented in this
paper.HLDA has been used as feature reduction technique
and also its impact on the recognition score has been
highlighted.