Open AccessTo Improve Voice Recognition System using GMM and HMM Classification Models
Author(s) -
Mrs. Sonali Nemade,
Yogesh Sharma,
Dr.Ranjit D. Patil
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.k2178.0981119
Subject(s) - hidden markov model , speech recognition , computer science , artificial intelligence , correctness , mixture model , context (archaeology) , speaker recognition , pronunciation , pattern recognition (psychology) , linguistics , paleontology , philosophy , biology , programming language
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