More Web Proxy on the site http://driver.im/

research-article

Large vocabulary continuous speech recognition for Urdu

Authors:

Sarmad Hussain,

Riffat Bokhari,

Rahila ParveenAuthors Info & Claims

FIT '10: Proceedings of the 8th International Conference on Frontiers of Information Technology

Article No.: 1, Pages 1 - 5

https://doi.org/10.1145/1943628.1943629

Published: 21 December 2010 Publication History

Abstract

This paper presents the development of acoustic and language models for robust Urdu speech recognition using the CMU Sphinx Open Source Toolkit for speech recognition. Three models have been developed incrementally, with the addition of speech data of up to two speakers per pass; one model using data from 40 female speakers only, one from 41 male speakers only, and one with both male and female speakers (81 speakers). This paper presents the current recognition results, and discusses approaches for improving these recognition rates.

References

[1]

M. P. Lewis (ed.). Ethnologue: Languages of the World, Sixteenth edition. Dallas, Tex.: SIL International, 2009. Online version: http://www.ethnologue.com/.

[2]

Government of Pakistan, Statistics Division, Federal Bureau of Statistics. Pakistan Statistical Yearbook 2009. www.statpak.gov.pk/depts/fbs/publications/yearbook2009/yearbook2009.html, accessed July 2010.

[3]

R. Cole (ed). Survey of the State of the Art in Human Language Technology. Cambridge University Press and Giardini, 1997.

Digital Library

[4]

A. Nakamura, S. Matsunaga, T. Shimizu, M. Tonomura, Y. Sagisaka. Japanese Speech Databases for Robust Speech Recognition. Proceedings of the ICSLP'96. Philadelphia, PA, pp. 2199--2202, Volume 4.

[5]

J. H. L. Hansen, R. Sarikaya, U. Yapanel and B. L. Pellom. Robust Speech Recognition in Noise: An Evaluation Using SPINE Corpus. Eurospeech '2001, Aalborg, Denmark, September 2001.

[6]

CMU Sphinx Open Source Toolkit for Speech Recognition Project by Carnegie Mellon University, http://cmusphinx.sourceforge.net/, accessed July 2010.

[7]

A. Raza, S. Hussain, H. Sarfraz, I. Ullah, Z. Sarfraz, "An ASR System for Spontaneous Urdu Speech". Submitted to O-COCOSDA 2010.

[8]

J. Ashraf, N. Iqbal, N. S. Khattak and A. M. Zaidi. Speaker Independent Urdu Speech Recognition Using HMM. In Proceedings, Natural Language Processing and Information Systems: 15th International Conference on Applications of Natural Language to Information Systems, Cardiff, UK. June 23--25, 2010.

Digital Library

[9]

H. Satori, H. Hiyassat, M. Harti and N. Chenfour. Investigation Arabic Speech Recognition using CMU Sphinx System. The International Arab Journal of Information Technology, Vol. 6, No. 2, April 2009.

[10]

M. U. Akram and M. Arif. Design of an Urdu Speech Recognizer Based Upon Acoustic Modeling Approach. In proceedings, IEEE INMIC 2004, pp. 91--96.

[11]

S. Azam, M. Mansoor, ZA. Shahzad, M. Mughal and S. Mohsin. Urdu Spoken Digits Recognition Using Classified MFCC and Backpropagation Neural Network. In proceedings, IEEE Computer Graphics, Imaging and Visualization, 2007.

Digital Library

[12]

A. Ahad, A. Fayyaz, T. Mehmood. Speech Recognition Using Multilayer Perceptron. In proceedings, Student Conference, ISCON, IEEE, 2002.

[13]

S. K. Hasnain. Recognizing Spoken Urdu Numbers Using Fourier Descriptor and Neural Networks with MATLAB. Second International Conference on Electrical Engineering, University of Engineering and Technology, Lahore, Pakistan. 25--26 March 2008.

[14]

C. Neti, N. Rajput and A. Verma. A Large Vocabulary Continuous Speech Recognition System for Hindi. In proceedings, 3rd IEEE Workshop on Multimedia Signal Processing, Copenhagen, 2002.

[15]

J. Kacur and G. Rozinaj. Practical Issues of Building Robust HMM Models using HTK and Sphinx Systems. In Speech Recognition, Technologies and Applications. F. Mihelic and J. Zibert. I-Tech (ed.), Vienna, Austria. November 2008.

[16]

HTK, http://htk.eng.cam.ac.uk, accessed July 2010.

[17]

Julius, http://julius.sourceforge.jp/en_index, accessed July 2010.

[18]

Institute for Signal and Information Processing (ISIP) Internet-Accessible Speech Recognition Technology Project, www.isip.piconepress.com/projects/speech, accessed July 2010.

[19]

H. Sarfraz, S. Hussain, R. Bokhari, A. A. Raza, I. Ullah, Z. Sarfraz, S. Pervez, A. Mustafa, I. Javed and R. Parveen. Speech Corpus Development for a Speaker Independent Spontaneous Urdu Speech Recognition System. O-COCOSDA 2010.

[20]

The CMU Statistical Language Modeling (SLM) Toolkit. www.speech.cs.cmu.edu/SLM_info.html, accessed July 2010.

[21]

Manual for the Sphinx3 Recognition System. www.speech.cs.cmu.edu/sphinxman, accessed July 2010.

[22]

Robust Group's Open Source Tutorial: Learning to Use the CMU Sphinx Automatic Speech Recognition System. www.speech.cs.cmu.edu/sphinx/tutorial.html, accessed July 2010.

[23]

X. Huang, A. Acero, H. W. Hon. Spoken Language Processing: A Guide to Theory, Algorithm and System Development. Prentice Hall, New Jersey, 2001.

Digital Library

[24]

S. Hussain. Letter-to-Sound Conversion for Urdu Text-to-Speech System. In proceedings, Workshop on Computational Approaches to Arabic Script-based Languages, COLING 2004, Geneva, Switzerland, 2004.

Digital Library

Cited By

Naqvi STahir MJaved KKhan HRaza ASaeed Z(2024)Code-Mixed Street Address Recognition and Accent Adaptation for Voice-Activated Navigation ServicesIEEE Access10.1109/ACCESS.2024.349661712(168393-168411)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3496617
Khullar ASantosh MKumar PRahman STripathi RKumar DSaini SPandey RSeth A(2021)Early Results from Automating Voice-based Question-Answering Services Among Low-income Populations in IndiaProceedings of the 4th ACM SIGCAS Conference on Computing and Sustainable Societies10.1145/3460112.3471946(79-87)Online publication date: 28-Jun-2021
https://dl.acm.org/doi/10.1145/3460112.3471946
Kumar YSingh NKumar MSingh A(2020)AutoSSR: an efficient approach for automatic spontaneous speech recognition model for the Punjabi LanguageSoft Computing10.1007/s00500-020-05248-1Online publication date: 10-Aug-2020
https://doi.org/10.1007/s00500-020-05248-1
Show More Cited By

Index Terms

Large vocabulary continuous speech recognition for Urdu
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Speech recognition

Recommendations

Modeling filled pauses for spontaneous speech recognition applications
AEE'08: Proceedings of the 7th WSEAS International Conference on Application of Electrical Engineering

This paper is focused on acoustic modeling for spontaneous speech recognition applications. This topic is still a very challenging task for speech technology research community. The attributes of spontaneous speech can heavily degrade speech recognizer'...
Toward Robust Speech Recognition and Understanding

The principal cause of speech recognition errors is a mismatch between trained acoustic/language models and input speech due to the limited amount of training data in comparison with the vast variation of speech. It is crucial to establish methods that ...
Combining Spectral Representations for Large-Vocabulary Continuous Speech Recognition

In this paper, we investigate the combination of complementary acoustic feature streams in large-vocabulary continuous speech recognition (LVCSR). We have explored the use of acoustic features obtained using a pitch-synchronous analysis, Straight, in ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

FIT '10: Proceedings of the 8th International Conference on Frontiers of Information Technology

December 2010

281 pages

ISBN:9781450303422

DOI:10.1145/1943628

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

HEC: Higher Education Commission, Pakistan
COMSATS Institute of Information Technology

In-Cooperation

SIGAI: ACM Special Interest Group on Artificial Intelligence

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 December 2010

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

FIT '10

Sponsor:

HEC

FIT '10: 8th International Conference on Frontiers of Information Technology

December 21 - 23, 2010

Islamabad, Pakistan

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

12
Total Citations
View Citations
414
Total Downloads

Downloads (Last 12 months)7
Downloads (Last 6 weeks)0

Reflects downloads up to 14 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Naqvi STahir MJaved KKhan HRaza ASaeed Z(2024)Code-Mixed Street Address Recognition and Accent Adaptation for Voice-Activated Navigation ServicesIEEE Access10.1109/ACCESS.2024.349661712(168393-168411)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3496617
Khullar ASantosh MKumar PRahman STripathi RKumar DSaini SPandey RSeth A(2021)Early Results from Automating Voice-based Question-Answering Services Among Low-income Populations in IndiaProceedings of the 4th ACM SIGCAS Conference on Computing and Sustainable Societies10.1145/3460112.3471946(79-87)Online publication date: 28-Jun-2021
https://dl.acm.org/doi/10.1145/3460112.3471946
Kumar YSingh NKumar MSingh A(2020)AutoSSR: an efficient approach for automatic spontaneous speech recognition model for the Punjabi LanguageSoft Computing10.1007/s00500-020-05248-1Online publication date: 10-Aug-2020
https://doi.org/10.1007/s00500-020-05248-1
Zia TZahid U(2019)Long short-term memory recurrent neural network architectures for Urdu acoustic modelingInternational Journal of Speech Technology10.1007/s10772-018-09573-722:1(21-30)Online publication date: 1-Mar-2019
https://dl.acm.org/doi/10.1007/s10772-018-09573-7
Manamperi WKarunathilake DMadhushani TGalagedara NDias D(2018)Sinhala Speech Recognition for Interactive Voice Response Systems Accessed Through Mobile Phones2018 Moratuwa Engineering Research Conference (MERCon)10.1109/MERCon.2018.8421888(241-246)Online publication date: May-2018
https://doi.org/10.1109/MERCon.2018.8421888
Londhe NKshirsagar G(2017)Continuous speech recognition system for chhattisgarhi2017 International Conference on Communication and Signal Processing (ICCSP)10.1109/ICCSP.2017.8286379(0365-0369)Online publication date: Apr-2017
https://doi.org/10.1109/ICCSP.2017.8286379
Qasim MNawaz SHussain SHabib T(2016)Urdu speech recognition system for district names of Pakistan: Development, challenges and solutions2016 Conference of The Oriental Chapter of International Committee for Coordination and Standardization of Speech Databases and Assessment Techniques (O-COCOSDA)10.1109/ICSDA.2016.7918979(28-32)Online publication date: Oct-2016
https://doi.org/10.1109/ICSDA.2016.7918979
Rauf SHameed AHabib THussain S(2015)District names speech corpus for Pakistani Languages2015 International Conference Oriental COCOSDA held jointly with 2015 Conference on Asian Spoken Language Research and Evaluation (O-COCOSDA/CASLRE)10.1109/ICSDA.2015.7357893(207-211)Online publication date: Oct-2015
https://doi.org/10.1109/ICSDA.2015.7357893
Ali HAhmad NZhou XIqbal KAli S(2014)DWT features performance analysis for automatic speech recognition of UrduSpringerPlus10.1186/2193-1801-3-2043:1Online publication date: 27-Apr-2014
https://doi.org/10.1186/2193-1801-3-204
Ali HAhmad NZhou XAli MManjotho A(2014)Linear Discriminant Analysis Based Approach for Automatic Speech Recognition of Urdu Isolated WordsCommunication Technologies, Information Security and Sustainable Development10.1007/978-3-319-10987-9_3(24-34)Online publication date: 11-Sep-2014
https://doi.org/10.1007/978-3-319-10987-9_3
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents