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Speech Recognition System Architecture for Gujarati Language

by Jinal H. Tailor, Dipti B. Shah
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 138 - Number 12
Year of Publication: 2016
Authors: Jinal H. Tailor, Dipti B. Shah
10.5120/ijca2016909049

Jinal H. Tailor, Dipti B. Shah . Speech Recognition System Architecture for Gujarati Language. International Journal of Computer Applications. 138, 12 ( March 2016), 28-31. DOI=10.5120/ijca2016909049

@article{ 10.5120/ijca2016909049,
author = { Jinal H. Tailor, Dipti B. Shah },
title = { Speech Recognition System Architecture for Gujarati Language },
journal = { International Journal of Computer Applications },
issue_date = { March 2016 },
volume = { 138 },
number = { 12 },
month = { March },
year = { 2016 },
issn = { 0975-8887 },
pages = { 28-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume138/number12/24433-2016909049/ },
doi = { 10.5120/ijca2016909049 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:39:31.301789+05:30
%A Jinal H. Tailor
%A Dipti B. Shah
%T Speech Recognition System Architecture for Gujarati Language
%J International Journal of Computer Applications
%@ 0975-8887
%V 138
%N 12
%P 28-31
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Speech recognition is an area of Natural Language Processing and Artificial Intelligence. To achieve good accuracy and efficiency of Automatic Speech Recognition (ASR) system for Indian Gujarati language is challenging task due to its morphology, language barriers, different dialects, and unavailability of resources. This paper presents proposed architecture of ASR for Gujarati language. Raw input data have been collected from 4 male and 2 female who belongs from age between 18 to 36 years to prepare dataset for training purpose. The goal of Speech recognition system is to make machines capable enough to operate in natural languages. ASR is a system to convert vocalized form to visualized form using different computational devices. This convincing approach is useful to the people having disabilities deaf or inability to use input device. In this paper we have used Hidden Markov Model Toolkit HTK Tool to measure performance and error parameters. The system implementation analyzed WR (Word Recognition Rate) 95.9% and WER (Word Error Rate) as 5.85 % in Lab environment. For the open noisy environment calculated WR was 95.1% and WER found 7.40%.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Acoustic Model Hidden Markov Model Gujarati Speech-To-Text

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