Graph Cut Based Segmentation Method for Tamil Continuous Speech

Sree, B.R. Laxmi; Vijaya, M. S.

doi:10.1007/978-981-10-3274-5_21

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Anfis Model For Tamil Phoneme Recognition1

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2019

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“…In this study, database "Kazhangiyam", developed in our earlier work is used for building a phoneme recognition model. The dataset used here is a collection of data points, where each data point represents the DWT features of a phoneme, which has been segmented from the continuous Tamil speech using a graph-cut based segmentation algorithm [19]. Each phoneme is defined as a data point with 90 DWT features.…”

Section: Anfis Model For Tamil Phoneme Recognitionmentioning

confidence: 99%

ANFIS for Tamil Phoneme Classification

B.R.*,

M.S.

2019

IJEAT

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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.

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Section: Anfis Model For Tamil Phoneme Recognitionmentioning

confidence: 99%