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Reseach Article

Automatic Speech Recognition of Urdu Digits with Optimal Classification Approach

by Hazrat Ali, An Jianwei, Khalid Iqbal
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 118 - Number 9
Year of Publication: 2015
Authors: Hazrat Ali, An Jianwei, Khalid Iqbal

Hazrat Ali, An Jianwei, Khalid Iqbal . Automatic Speech Recognition of Urdu Digits with Optimal Classification Approach. International Journal of Computer Applications. 118, 9 ( May 2015), 1-5. DOI=10.5120/20770-3275

@article{ 10.5120/20770-3275,
author = { Hazrat Ali, An Jianwei, Khalid Iqbal },
title = { Automatic Speech Recognition of Urdu Digits with Optimal Classification Approach },
journal = { International Journal of Computer Applications },
issue_date = { May 2015 },
volume = { 118 },
number = { 9 },
month = { May },
year = { 2015 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { },
doi = { 10.5120/20770-3275 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T23:01:11.909072+05:30
%A Hazrat Ali
%A An Jianwei
%A Khalid Iqbal
%T Automatic Speech Recognition of Urdu Digits with Optimal Classification Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 118
%N 9
%P 1-5
%D 2015
%I Foundation of Computer Science (FCS), NY, USA

Speech Recognition for Urdu language is an interesting and less developed task. This is primarily due to the fact that linguistic resources such as rich corpus are not available for Urdu. Yet, few attempts have been made for developing Urdu speech recognition frameworks using the traditional approaches such as Hidden Markov Models and Neural Networks. In this work, we investigate the use of three classification methods for Urdu speech recognition task. We extract the Mel Frequency Cepstral Coefficients, the delta and delta-delta features from the speech data and train the classifiers to perform Urdu speech recognition. We present the performance achieved by training a Support Vector Machine (SVM) classifier, a random forest (RF) classifier and a linear discriminant analysis classifier (LDA) for comparison with SVM. Consequently, the experimental results show that SVM gives better performance than RF and LDA classifiers on this particular task.

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Index Terms

Computer Science
Information Sciences


Linear Discriminant Analysis Mel-Frequency Cepstral Coefficients Random Forest Support Vector Machines Urdu