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Acoustics Speech Processing of Sanskrit Language

by Sujay G. Kakodkar, Samarth Borkar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 180 - Number 38
Year of Publication: 2018
Authors: Sujay G. Kakodkar, Samarth Borkar
10.5120/ijca2018917017

Sujay G. Kakodkar, Samarth Borkar . Acoustics Speech Processing of Sanskrit Language. International Journal of Computer Applications. 180, 38 ( May 2018), 27-32. DOI=10.5120/ijca2018917017

@article{ 10.5120/ijca2018917017,
author = { Sujay G. Kakodkar, Samarth Borkar },
title = { Acoustics Speech Processing of Sanskrit Language },
journal = { International Journal of Computer Applications },
issue_date = { May 2018 },
volume = { 180 },
number = { 38 },
month = { May },
year = { 2018 },
issn = { 0975-8887 },
pages = { 27-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume180/number38/29380-2018917017/ },
doi = { 10.5120/ijca2018917017 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:03:02.145368+05:30
%A Sujay G. Kakodkar
%A Samarth Borkar
%T Acoustics Speech Processing of Sanskrit Language
%J International Journal of Computer Applications
%@ 0975-8887
%V 180
%N 38
%P 27-32
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Speech processing (SP) is the latest trend in technology. An intelligent and precise human-machine interaction (HMI) is designed to engineer an automated, smart and secure application for household and commercial application. The existing methods highlight the absence of the speech processing in the under-resourced languages. The novelty of this work is that it presents a study of acoustic speech processing (ASP) using spectral components of Mel frequency cepstrum coefficient (MFCC) of Sanskrit language. A customized speech database is created as no generic database is available in Sanskrit. The processing method includes speech signal isolation, feature selection and extraction of selected features for applications. The speech is processed over a custom dataset consisting of Sanskrit speech corpus. The spectral features are calculated over 13 coefficients providing improved performance. The results obtained highlight the performance of the proposed system with the variation of the lifter parameter.

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Index Terms

Computer Science
Information Sciences

Keywords

Speech processing Human-machine interaction Mel frequency cepstrum coefficient Sanskrit language

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