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Comparative Analysis of LPCC, MFCC and BFCC for the Recognition of Hindi Words using Artificial Neural Networks

by Taabish Gulzar, Anand Singh, Sandeep Sharma
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 101 - Number 12
Year of Publication: 2014
Authors: Taabish Gulzar, Anand Singh, Sandeep Sharma
10.5120/17740-8271

Taabish Gulzar, Anand Singh, Sandeep Sharma . Comparative Analysis of LPCC, MFCC and BFCC for the Recognition of Hindi Words using Artificial Neural Networks. International Journal of Computer Applications. 101, 12 ( September 2014), 22-27. DOI=10.5120/17740-8271

@article{ 10.5120/17740-8271,
author = { Taabish Gulzar, Anand Singh, Sandeep Sharma },
title = { Comparative Analysis of LPCC, MFCC and BFCC for the Recognition of Hindi Words using Artificial Neural Networks },
journal = { International Journal of Computer Applications },
issue_date = { September 2014 },
volume = { 101 },
number = { 12 },
month = { September },
year = { 2014 },
issn = { 0975-8887 },
pages = { 22-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume101/number12/17740-8271/ },
doi = { 10.5120/17740-8271 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:31:29.819806+05:30
%A Taabish Gulzar
%A Anand Singh
%A Sandeep Sharma
%T Comparative Analysis of LPCC, MFCC and BFCC for the Recognition of Hindi Words using Artificial Neural Networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 101
%N 12
%P 22-27
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Most important way of communication among humans is language and primary medium used for the said is speech. The speech recognizers make use of a parametric form of a signal to obtain the most important distinguishable features of speech signal for recognition purpose. In this paper, Linear Prediction Cepstral Coefficient (LPCC), Mel Frequency Cepstral Coefficient (MFCC) and Bark frequency Cepstral coefficient (BFCC) feature extraction techniques for recognition of Hindi Isolated, Paired and Hybrid words have been studied and the corresponding recognition rates are compared. Artifical Neural Network is used as back end processor. The experimental results show that the better recognition rate is obtained for MFCC as compared to LPCC and BFCC for all the three types of words.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Hindi Hybrid words Spoken Paired words Feature Extraction Artifical Neural Networks.

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