Open AccessSpeech Recognition by Integrating Hidden Markov Model Correlated with Artificial Neural Network
Author(s) -
Kadam Sarika Shamrao*,
A. Muthukumaravel
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.b7769.129219
Subject(s) - hidden markov model , dynamic time warping , computer science , speech recognition , dynamic bayesian network , artificial neural network , artificial intelligence , markov model , process (computing) , bayesian probability , speech synthesis , markov chain , pattern recognition (psychology) , machine learning , operating system
Now every day's speech recognition is utilized broadly in numerous packages. In software program engineering and electric constructing, speech recognition (SR) is the interpretation of verbally expressed words into textual content. it's miles otherwise referred to as "computerized speech recognition" (CSR), "pc speech reputation", or most effective "speech to text" (STT). A hid Markov model (HMM) is a measurable Markov model wherein the framework being verified is notion to be a Markov process with in mystery (shrouded) states. A HMM may be introduced as the least hard dynamic Bayesian system. Dynamic time warping (DTW) is a truly understood strategy to locate a really perfect arrangement among two given (time-subordinate) groupings underneath sure confinements instinctively; the groupings are distorted in a nonlinear manner to coordinate each other. ANN is non-immediately statistics driven self-versatile methodology. it can distinguish and research co-related examples between information dataset and evaluating target esteems. Within the wake of preparing ANN may be utilized to anticipate the end result of new unfastened facts.