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DWT features performance analysis for automatic speech recognition of Urdu

This paper presents the work on Automatic Speech Recognition of Urdu language, using a comparative analysis for Discrete Wavelets Transform (DWT) based features and Mel Frequency Cepstral Coefficients (MFCC). These features have been extracted for one hundred isolated words of Urdu, each word uttere...

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Detalles Bibliográficos
Autores principales: Ali, Hazrat, Ahmad, Nasir, Zhou, Xianwei, Iqbal, Khalid, Ali, Sahibzada Muhammad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4320178/
https://www.ncbi.nlm.nih.gov/pubmed/25674450
http://dx.doi.org/10.1186/2193-1801-3-204
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author Ali, Hazrat
Ahmad, Nasir
Zhou, Xianwei
Iqbal, Khalid
Ali, Sahibzada Muhammad
author_facet Ali, Hazrat
Ahmad, Nasir
Zhou, Xianwei
Iqbal, Khalid
Ali, Sahibzada Muhammad
author_sort Ali, Hazrat
collection PubMed
description This paper presents the work on Automatic Speech Recognition of Urdu language, using a comparative analysis for Discrete Wavelets Transform (DWT) based features and Mel Frequency Cepstral Coefficients (MFCC). These features have been extracted for one hundred isolated words of Urdu, each word uttered by ten different speakers. The words have been selected from the most frequently used words of Urdu. A variety of age and dialect has been covered by using a balanced corpus approach. After extraction of features, the classification has been achieved by using Linear Discriminant Analysis. After the classification task, the confusion matrix obtained for the DWT features has been compared with the one obtained for Mel-Frequency Cepstral Coefficients based speech recognition. The framework has been trained and tested for speech data recorded under controlled environments. The experimental results are useful in determination of the optimum features for speech recognition task.
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spelling pubmed-43201782015-02-11 DWT features performance analysis for automatic speech recognition of Urdu Ali, Hazrat Ahmad, Nasir Zhou, Xianwei Iqbal, Khalid Ali, Sahibzada Muhammad Springerplus Research This paper presents the work on Automatic Speech Recognition of Urdu language, using a comparative analysis for Discrete Wavelets Transform (DWT) based features and Mel Frequency Cepstral Coefficients (MFCC). These features have been extracted for one hundred isolated words of Urdu, each word uttered by ten different speakers. The words have been selected from the most frequently used words of Urdu. A variety of age and dialect has been covered by using a balanced corpus approach. After extraction of features, the classification has been achieved by using Linear Discriminant Analysis. After the classification task, the confusion matrix obtained for the DWT features has been compared with the one obtained for Mel-Frequency Cepstral Coefficients based speech recognition. The framework has been trained and tested for speech data recorded under controlled environments. The experimental results are useful in determination of the optimum features for speech recognition task. Springer International Publishing 2014-04-27 /pmc/articles/PMC4320178/ /pubmed/25674450 http://dx.doi.org/10.1186/2193-1801-3-204 Text en © Ali et al.; licensee Springer. 2014 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.
spellingShingle Research
Ali, Hazrat
Ahmad, Nasir
Zhou, Xianwei
Iqbal, Khalid
Ali, Sahibzada Muhammad
DWT features performance analysis for automatic speech recognition of Urdu
title DWT features performance analysis for automatic speech recognition of Urdu
title_full DWT features performance analysis for automatic speech recognition of Urdu
title_fullStr DWT features performance analysis for automatic speech recognition of Urdu
title_full_unstemmed DWT features performance analysis for automatic speech recognition of Urdu
title_short DWT features performance analysis for automatic speech recognition of Urdu
title_sort dwt features performance analysis for automatic speech recognition of urdu
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4320178/
https://www.ncbi.nlm.nih.gov/pubmed/25674450
http://dx.doi.org/10.1186/2193-1801-3-204
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