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Please use this identifier to cite or link to this item: http://hdl.handle.net/10525/1111

Title: Improved Hybrid Model of HMM/GMM for Speech Recognition
Authors: Bansal, Poonam
Kant, Anuj
Kumar, Sumit
Sharda, Akash
Gupta, Shitij
Keywords: Speech Recognition
GMM
HMM
Pattern Recognition
Issue Date: 2008
Publisher: Institute of Information Theories and Applications FOI ITHEA
Abstract: In this paper, we propose a speech recognition engine using hybrid model of Hidden Markov Model (HMM) and Gaussian Mixture Model (GMM). Both the models have been trained independently and the respective likelihood values have been considered jointly and input to a decision logic which provides net likelihood as the output. This hybrid model has been compared with the HMM model. Training and testing has been done by using a database of 20 Hindi words spoken by 80 different speakers. Recognition rates achieved by normal HMM are 83.5% and it gets increased to 85% by using the hybrid approach of HMM and GMM.
URI: http://hdl.handle.net/10525/1111
ISSN: 1313-0455
Appears in Collections:Book 5 Intelligent Technologies and Applications

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