Continuous Density Hidden Markov Model for Hindi Speech Recognition

Continuous Density Hidden Markov Model for Hindi Speech Recognition

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 no...

Hybrid neural network/hidden Markov model continuous-speech recognition

n M In this paper we present a hybrid multilayer perceptron (MLP)/hidde arkov model (HMM) speaker-independent continuous-speech recognib tion system, in which the advantages of both approaches are combined y using MLPs to estimate the state-dependent observation probabilities p of an HMM. New MLP architectures and training procedures are resented which allow the modeling of multiple distributio...

Hidden Markov Model for Speech Recognition

In this paper, a theoretical framework for Bayesian adaptive training of the parameters of discrete hidden Markov model (DHMM) and of semi-continuous HMM (SCHMM) with Gaussian mixture state observation densities is presented. In addition to formulating the forward-backward MAP (maximum a posterion’) and the segmental MAP algorithms for estimating the above HMM parameters, a computationally effi...

Improved Hidden Markov Modeling for Speaker-Independent Continuous Speech Recognition

This paper reports recent efforts to further improve the performance of the Sphinx system for speaker-independent continuous speech recognition. The recognition error rate is significantly reduced with incorporation of additional dynamic features, semi-continuous hidden Markov models, and speaker clustering. For the June 1990 (RM2) evaluation test set, the error rates of our current system are ...

Speaker Independent Speech Recognition Using Hidden Markov Models for Persian Isolated Words