Title :
Hybrid HMM/ANN based isolated Hindi word recognition
Author :
Kapse, Y. ; Londhe, N.D.
Author_Institution :
Dept. of Electr. Eng., Nat. Inst. of Technol. Raipur, Raipur, India
Abstract :
Automatic speech recognition has become one of the most challenging task in the field of pattern recognition and natural language processing. In this paper, a hybrid model is proposed for isolated Hindi word recognition. This hybrid model involves the iterative training procedure. HMM is employed to induce the state transition probability distribution and ANN is employed as a classifier. HMM is designed by 4-state left to right model. In the proposed model ten Hindi words are used for the samples and five speakers for training and five distinct speakers for testing purpose and therefore the performance has achieved upto 89.8%.
Keywords :
hidden Markov models; natural language processing; neural nets; signal classification; speech recognition; statistical distributions; Hidden; artificial neural network; automatic speech recognition; classifier; hidden Markov model; hybrid HMM-ANN based isolated Hindi word recognition; iterative training procedure; natural language processing; pattern recognition; state transition probability distribution; Artificial neural networks; Automatic speech recognition; Hidden Markov models; Speech; Text recognition; Artificial Neural Network (ANN); Hidden Markov Model (HMM); Speech recognition; iterative training procedure; state transition probability distribution;
Conference_Titel :
India Conference (INDICON), 2014 Annual IEEE
Conference_Location :
Pune
Print_ISBN :
978-1-4799-5362-2
DOI :
10.1109/INDICON.2014.7030373
