ANFIS for Tamil Phoneme Classification
Laxmi Sree B.R.1, Vijaya M.S.2

1Laxmi Sree B.R.*, Department of Computer Science, Dr. G.R. Damodaran College of Science, Coimbatore, India.
2Vijaya M.S., Department of Computer Science, PSGR Krishnammal College for Women, Coimbatore, India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 2907-2914 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8804088619/2019©BEIESP | DOI: 10.35940/ijeat.F8804.088619
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Abstract: Phoneme recognition is an intricate problem lying under non-linear systems. Most research in this area revolve around try to model the pattern of features observed in the speech spectra with the use of Hidden Markov Models (HMM), various types of neural networks like deep recurrent neural networks, time delay neural networks, etc. for efficient phoneme recognition. In this paper, we study the effectiveness of the hybrid architecture, the Adaptive Neuro-Fuzzy Inference System (ANFIS) for capturing the spectral features of the speech signal to handle the problem of Phoneme Recognition. In spite of a wide range of research in this field, here we examine the power of ANFIS for least explored Tamil phoneme recognition problem. The experimental results have shown the ability of the model to learn the patterns associated with various phonetic classes, indicated with recognition improvement in terms of accuracy to its counterparts.
Keywords: Adaptive Neuro-Fuzzy Inference System, Phoneme Recognition, Speech Recognition, Tamil Phoneme Classification.