Research Article
A Neuro Fuzzy Classifier with Linguistic Hedges for Speech Recognition
@ARTICLE{10.4108/eai.13-7-2018.164114, author={Vani H Y and Anusuya M A}, title={A Neuro Fuzzy Classifier with Linguistic Hedges for Speech Recognition}, journal={EAI Endorsed Transactions on Internet of Things}, volume={5}, number={20}, publisher={EAI}, journal_a={IOT}, year={2019}, month={10}, keywords={}, doi={10.4108/eai.13-7-2018.164114} }- Vani H Y
Anusuya M A
Year: 2019
A Neuro Fuzzy Classifier with Linguistic Hedges for Speech Recognition
IOT
EAI
DOI: 10.4108/eai.13-7-2018.164114
Abstract
Fuzzy classification is the task of partitioning a feature space into fuzzy classes. A Neuro fuzzy classifier with linguistic hedges is proposed for noisy and clean speech classification. The linguistic Hedges are used to improve the meaning of fuzzy rules up to secondary level. Fuzzy entropy is applied to select optimal features of MFCC for framing the rules for designing the fuzzy inference system. Results obtained from the proposed classifier is compared over conventional and Neuro Fuzzy Classifier. The classification rates of the proposed model is better than other traditional and conventional fuzzy classifiers. 0.22 to 5% improved classification accuracy is observed for the FSDD dataset. And 5% to 11% of improved classification accuracy is observed for Kannada dataset. From this study it is identified that LH plays a major role in classifying the overlapped classes of data.
Copyright © 2019 Vani H Y et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
