Please use this identifier to cite or link to this item: http://localhost:8080/xmlui/handle/123456789/3896
Title: ANFIS FOR TAMIL PHONEME CLASSIFICATION
Authors: Laxmi Sree, B R
Vijaya, M S
Keywords: Adaptive Neuro-Fuzzy Inference System
Phoneme Recognition
Speech Recognition
Tamil Phoneme Classification
Issue Date: Aug-2019
Publisher: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP)
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.
URI: https://www.ijeat.org/wp-content/uploads/papers/v8i6/F8804088619.pdf
Appears in Collections:f) 2019-Scopus Open Access (PDF)

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