Speech Recognition by Integrating Hidden Markov Model Correlated with Artificial Neural Network
Kadam Sarika Shamrao1, A Muthukumaravel2

1Kadam Sarika Shamrao*, Research Scholar, Department of Computer Applications, BIHER – Bharath Institute of Higher Education and Research, Chennai, India.
2A Muthukumaravel, Dean, Arts & Science, BIHER – Bharath Institute of Higher Education and Research, Chennai, India

Manuscript received on November 17, 2019. | Revised Manuscript received on 26 November, 2019. | Manuscript published on December 10, 2019. | PP: 3892-3895 | Volume-9 Issue-2, December 2019. | Retrieval Number: B7769129219/2019©BEIESP | DOI: 10.35940/ijitee.B7769.129219
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Abstract: 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. 
Keywords:  SR, HMM, DTW, ANN
Scope of the Article: Pattern Recognition