CFP last date
22 April 2024
Call for Paper
May Edition
IJCA solicits high quality original research papers for the upcoming May edition of the journal. The last date of research paper submission is 22 April 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Analysis of Different Feature for Language Identification

by Snehal V. Gite, J. V. Shinde
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 146 - Number 10
Year of Publication: 2016
Authors: Snehal V. Gite, J. V. Shinde
10.5120/ijca2016910924

Snehal V. Gite, J. V. Shinde . Analysis of Different Feature for Language Identification. International Journal of Computer Applications. 146, 10 ( Jul 2016), 10-14. DOI=10.5120/ijca2016910924

@article{ 10.5120/ijca2016910924,
author = { Snehal V. Gite, J. V. Shinde },
title = { Analysis of Different Feature for Language Identification },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2016 },
volume = { 146 },
number = { 10 },
month = { Jul },
year = { 2016 },
issn = { 0975-8887 },
pages = { 10-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume146/number10/25433-2016910924/ },
doi = { 10.5120/ijca2016910924 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:50:03.406913+05:30
%A Snehal V. Gite
%A J. V. Shinde
%T Analysis of Different Feature for Language Identification
%J International Journal of Computer Applications
%@ 0975-8887
%V 146
%N 10
%P 10-14
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Language Identification is the task of identifying language spoken from unknown user. The main objective is to achieve accurate results in shortest speech segments by using automatic Language Identification system. It works on language classification that involves new language rapid learning identities and reduce the computational complexity. MFCC, GFCC, PLP and the combination of these feature are consider in language identification system. The proposed approach that transforms the spoken words to a represent low dimensional i-vector, on which classification techniques are applied. Feature extraction is done on input audio, Universal background model and i-vector extraction are used in proposed system in order to meet the challenges involved in rapidly making reliable decisions about the spoken language such as Marathi, Hindi and English. For the relevant languages under the different acoustic condition are used to capture robust feature extraction scheme.

References
  1. Y.K. Muthusamy,” A Segment Approach to Automatic language identification uses the telephone speech corpus” 1987
  2. Y. K. Muthusamy, N. Jain, and R. A. Cole, “Perceptual benchmarks for automatic language identification,” Proc. ICASSP, vol. 1, pp. I–333, 1994..
  3. Y. K. Muthusamy, E. Barnard, and R. A. Cole,“Reviewing automatic language identification,” IEEE Signal Process. Mag., vol. 11, no. 4, pp.33–41, Oct. 1994.
  4. M. A. Zissman, “Language identification using phoneme recognition and phonotactic language modeling,” in Proc. ICASSP, 1995, vol. 5, pp. 3503–3506.
  5. Y. Yan and E. Barnard, “An approach to automatic language identification based on language-dependent phone recognition,” in Proc. ICASSP, 1995, vol. 5, pp. 3511–3514
  6. W. M. Campbell, D. E. Sturim, D. A. Reynolds, and A. Solomonoff, “SVM based speaker verification using a GMM supervector kernel and NAP variability compensation,” in Proc. ICASSP, 2006, vol. 1, pp. 97–100
  7. E. Singer, P. A. Torres-Carrasquillo, T. P. Gleason, W. M. Campbell, and D. A. Reynolds, “Acoustic, phonetic, and discriminative approaches to automatic language identification,” in Proc. Interspeech, 2003
  8. N. Dehak, P. J. Kenny, R. Dehak, P. Dumouchel, and P. Ouellet, “Front-end factor analysis for speaker verification,” IEEE Trans. Audio, Speech, Lang. Process., vol. 19, no. 4, pp. 788–798, May 2011
  9. N. Dehak, P. J. Kenny, R. Dehak, P. Dumouchel, and P. Ouellet, “Front-end factor analysis for speaker verification,” IEEE Trans. Audio, Speech, Lang. Process., vol. 19, no. 4, pp. 788–798, May 2011.
  10. A. Larcher, P. Bousquet, K. A. Lee, D. Matrouf, H. Li, and J.-F. Bonastre, “I-vectors in the context of phonetically-constrained short utterances for speaker verification,” in Proc. ICASSP, 2012, pp. 4773–4776. ]
  11. P. A. Torres-Carrasquillo, D. A. Reynolds, and J. Deller, Jr., “Language identification using gaussian mixture model tokenization,” in Proc. ICASSP, 2002, vol. 1, pp. I–757.
  12. P. Kenny, G. Boulianne, P. Ouellet, and P. Dumouchel, “Joint factor analysis versus eigenchannels in speaker recognition,” IEEE Trans.Audio, Speech, Lang. Process., vol 15, no. 4, pp. 1435–1447, May 2007
  13. A. Kanagasundaram, R. Vogt, D. B. Dean, S. Sridharan, and M. W. Mason, “I-vector based speaker recognition on short utterances,” in Proc. Interspeech, 2011, pp. 2341–2344.
  14. A. Larcher, P. Bousquet, K. A. Lee, D. Matrouf, H. Li, and J.-F. Bonastre, “I-vectors in the context of phonetically-constrained short utterances for speaker verification,” in Proc. ICASSP, 2012, pp. 4773–4776.
  15. M.V. Segbroeck, Ruchir Travadi, Shrikanth S. Narayanan,” Rapid language identification,” ieee transactions on audio, speech, and language processing, vol. 23, no. 7, july 2015
Index Terms

Computer Science
Information Sciences

Keywords

Language Identification Feature Extraction Universal background model.

Powered by PhDFocusTM