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Continuous Speech Recognition for Punjabi Language

by Wiqas Ghai, Navdeep Singh
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 72 - Number 14
Year of Publication: 2013
Authors: Wiqas Ghai, Navdeep Singh
10.5120/12563-9002

Wiqas Ghai, Navdeep Singh . Continuous Speech Recognition for Punjabi Language. International Journal of Computer Applications. 72, 14 ( June 2013), 23-28. DOI=10.5120/12563-9002

@article{ 10.5120/12563-9002,
author = { Wiqas Ghai, Navdeep Singh },
title = { Continuous Speech Recognition for Punjabi Language },
journal = { International Journal of Computer Applications },
issue_date = { June 2013 },
volume = { 72 },
number = { 14 },
month = { June },
year = { 2013 },
issn = { 0975-8887 },
pages = { 23-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume72/number14/12563-9002/ },
doi = { 10.5120/12563-9002 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:37:55.391177+05:30
%A Wiqas Ghai
%A Navdeep Singh
%T Continuous Speech Recognition for Punjabi Language
%J International Journal of Computer Applications
%@ 0975-8887
%V 72
%N 14
%P 23-28
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Punjabi language is a tonal language belonging to an Indo-Aryan language family and has number of speakers all around the world. Punjabi language has gained acceptability in the media & communication and thereby deserves to get a pace in the growing field of automatic speech recognition which has been explored already for number of other Indian and foreign languages successfully. Some work has been done in the field of isolated word and connected word speech recognition for Punjabi language. Acoustic template matching and Vector quantization have been the supporting techniques. Continuous speech recognition is one area where no work has been done so far for Punjabi language. In this paper, an effort has been made to build automatic speech recognizer to recognize continuous speech sentences by using Tri-Phone based acoustic modeling approach on HTK 3. 4. 1 speech engine. Overall recognition accuracy has been found to be 82. 18% at sentence level and 94. 32% at word level.

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Index Terms

Computer Science
Information Sciences

Keywords

Tri-Phones ASR Hidden Markov Model MLF Acoustic Model HTK Gaussian Mixtures

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