Fuzzy Utilization in Speech Recognition and its Different Application 
Bennilo Fernandes. J1, Kasi Prasad Mannepalli2, Agilesh Saravanan3, KTPS Kumar4
1J.Bennilo Fernandes, Asst. Professor, Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Guntur (A.P), India.
2Kasiprasad Mannepalli, Assoc. Professor, Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation K L University, Vijayawada (A.P), India.
3R.Agilesh Saravanan, Asst. Professor, Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation K L University, Vijayawada (A.P), India.
4K.T.P.S Kumar, Asst. Professor, Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation K L University, Vijayawada (A.P), India.
Manuscript received on 25 August 2019 | Revised Manuscript received on 01 September 2019 | Manuscript Published on 14 September 2019 | PP: 261-266 | Volume-8 Issue-5S3, July 2019 | Retrieval Number: E10580785S319/19©BEIESP | DOI: 10.35940/ijeat.E1058.0785S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Talk affirmation is one among the basic zones in cutting edge talk process. The examination of talk affirmation may be a bit of an examination for “artificial intelligence” machines that may “hear” and “appreciate” the verbally communicated data. The customary ways for talk affirmation like HMM and DTW, are outrageously inconvenient and time excellent. As such formal Fuzzy justification may be an endeavor in cutting edge talk process for the convincing portrayal of talk affirmation in a couple of utilization. The approach masterminded in the midst of this paper streamlines the utilization of fuzzy in talk affirmation and make the data dealing with time shorter. The case considered in the midst of this paper is that the least mind boggling, i.e., the example of speaker dependence, little vocabulary and disconnected words. There are various spectral and common choices isolated from human talk. The present ways for tendency acknowledgment from voice use basically MFCC and Energy feature. This paper briefs an overview concerning the present work on talk feeling ID strong for completing more examination by feathery approach.
Keywords: Emotional Speech Recognition, Fuzzy, HMM, NN, , Applications.
Scope of the Article: Pattern Recognition