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Highly Training Algorithm for Enhancement of Speech Signal Data (HTA-ESSD)

by Kumbhar Trupti Sambhaji, Veena C.S.
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 174 - Number 24
Year of Publication: 2021
Authors: Kumbhar Trupti Sambhaji, Veena C.S.
10.5120/ijca2021921137

Kumbhar Trupti Sambhaji, Veena C.S. . Highly Training Algorithm for Enhancement of Speech Signal Data (HTA-ESSD). International Journal of Computer Applications. 174, 24 ( Mar 2021), 1-5. DOI=10.5120/ijca2021921137

@article{ 10.5120/ijca2021921137,
author = { Kumbhar Trupti Sambhaji, Veena C.S. },
title = { Highly Training Algorithm for Enhancement of Speech Signal Data (HTA-ESSD) },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2021 },
volume = { 174 },
number = { 24 },
month = { Mar },
year = { 2021 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume174/number24/31819-2021921137/ },
doi = { 10.5120/ijca2021921137 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:22:56.820131+05:30
%A Kumbhar Trupti Sambhaji
%A Veena C.S.
%T Highly Training Algorithm for Enhancement of Speech Signal Data (HTA-ESSD)
%J International Journal of Computer Applications
%@ 0975-8887
%V 174
%N 24
%P 1-5
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The enhancement of speech aims to maximize the quality of speech by utilizing HTA (Highly Training Algorithm). The main aim of enhancement is to maximize the intelligibility or perceptual quality of the speech signal data. We represent HTA, aimed at fast removal and very effective of background noise from the signal-channel of speech signal data based on analytically determined output-weights and randomly selected-hidden units. The feature learning with HTA may not be effective for the natural signals, even with the larger number of the hidden nodes, C-HTA (Classified Highly Training Algorithm) are employed by leveraging the sparse auto-encoders. This work is mainly to apply C-HTA and HTA to enhance the speech-signal data. The proposed HTA is evaluated on Aurora database at three SNRs. We also compare our introduced algorithm with many state-art-methods.

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Index Terms

Computer Science
Information Sciences

Keywords

Enhancement of speech HTA C-HTA

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