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Reseach Article

Automated Transcription System for Malayalam Language

by Cini Kurian, Kannan Balakrishnan
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 19 - Number 5
Year of Publication: 2011
Authors: Cini Kurian, Kannan Balakrishnan
10.5120/2360-3091

Cini Kurian, Kannan Balakrishnan . Automated Transcription System for Malayalam Language. International Journal of Computer Applications. 19, 5 ( April 2011), 5-10. DOI=10.5120/2360-3091

@article{ 10.5120/2360-3091,
author = { Cini Kurian, Kannan Balakrishnan },
title = { Automated Transcription System for Malayalam Language },
journal = { International Journal of Computer Applications },
issue_date = { April 2011 },
volume = { 19 },
number = { 5 },
month = { April },
year = { 2011 },
issn = { 0975-8887 },
pages = { 5-10 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume19/number5/2360-3091/ },
doi = { 10.5120/2360-3091 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:06:10.780747+05:30
%A Cini Kurian
%A Kannan Balakrishnan
%T Automated Transcription System for Malayalam Language
%J International Journal of Computer Applications
%@ 0975-8887
%V 19
%N 5
%P 5-10
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Malayalam is one of the 22 scheduled languages in India with more than 130 million speakers. This paper presents a report on the development of a speaker independent, continuous transcription system for Malayalam. The system employs Hidden Markov Model (HMM) for acoustic modeling and Mel Frequency Cepstral Coefficient (MFCC) for feature extraction. It is trained with 21 male and female speakers in the age group ranging from 20 to 40 years. The system obtained a word recognition accuracy of 87.4% and a sentence recognition accuracy of 84%, when tested with a set of continuous speech data.

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Index Terms

Computer Science
Information Sciences

Keywords

HMM MFCC Speech Recognition Transcription systems