To Improve Voice Recognition System using GMM and HMM Classification Models
Sonali Nemade1, Yogesh Kumar Sharma2, Ranjit D. Patil3

1Mrs. Sonali Nemade, Assistant Professor Dr. D. Y. Patil Arts , Commerce and Science College Pimpri , Pune, India.
2Dr. Yogesh Kumar Sharma, Associate Professor and Research Coordinator, Department of Computer Science and Engineering, Shri JJT University, Jhunjhunu, Rajasthan, India.
3Dr. Ranjit D. Patil, Vice-Principal and H.O.D in Dr. D.Y. Patil ACS College, Pimpri, Pune, India.

Manuscript received on 21 August 2019. | Revised Manuscript received on 02 September 2019. | Manuscript published on 30 September 2019. | PP: 2724-2726 | Volume-8 Issue-11, September 2019. | Retrieval Number: K21780981119/2019©BEIESP | DOI: 10.35940/ijitee.K2178.0981119
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Abstract: In this paper, the researcher study automatic speech recognition technology for the individual. We propose a new voice recognition system using a hybrid model GMM-HMM. HMM and GMM is a non-linear classification model. Each state in an HMM can be thought of as a GMM. HMM is consider observation for state. It is also known as time series classification model. In this model, samples have been trained independently and parameters consider jointly which provides better performance than other classification models. Speech recognition system consider two types of learning patterns such as supervised learning and unsupervised learning. In this context speaker dependent and speaker independent used for identifying the efficient and effective voice. In this paper researcher considered supervised learning model for recognize efficient voice. This new voice recognition system identifies incorrect phonemes and verifies the correctness of voice pronunciation. Using the GMM-HMM hybrid model produces better performance and effectiveness of voice.
Keywords: GMM classifier, HMM classifier, MFCC, deep neural network, artificial Intelligence..
Scope of the Article: Classification