CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Speech Recognition System to Leverage the Accuracy of Training Sample using Optimized Matching Window

by Gunjan Thakur, Anudeep Goraya
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 124 - Number 15
Year of Publication: 2015
Authors: Gunjan Thakur, Anudeep Goraya
10.5120/ijca2015905670

Gunjan Thakur, Anudeep Goraya . Speech Recognition System to Leverage the Accuracy of Training Sample using Optimized Matching Window. International Journal of Computer Applications. 124, 15 ( August 2015), 23-28. DOI=10.5120/ijca2015905670

@article{ 10.5120/ijca2015905670,
author = { Gunjan Thakur, Anudeep Goraya },
title = { Speech Recognition System to Leverage the Accuracy of Training Sample using Optimized Matching Window },
journal = { International Journal of Computer Applications },
issue_date = { August 2015 },
volume = { 124 },
number = { 15 },
month = { August },
year = { 2015 },
issn = { 0975-8887 },
pages = { 23-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume124/number15/22181-2015905670/ },
doi = { 10.5120/ijca2015905670 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:14:30.596728+05:30
%A Gunjan Thakur
%A Anudeep Goraya
%T Speech Recognition System to Leverage the Accuracy of Training Sample using Optimized Matching Window
%J International Journal of Computer Applications
%@ 0975-8887
%V 124
%N 15
%P 23-28
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this voice recognition system is to recognize the voice samples spoken by human and recognize over the system. In this select the most commanly used features of the voice samples with the help of MFCC(Mel frequency cofficient ceptrum) and that feature match with the real time voice sample features using DTW(Dynamic time wrapping) and it is accepted by the system.

References
  1. Yogesh Kumar Sen, R. K. Chaurasiya. IEEE International Conference on voice rcoginition-june2014,24:58-95.
  2. Daubechies, I. The wavelet transform, time-frequency localization andsignal analysis. IEEETransformation and Information Theory.2014,36: 961-1005.
  3. Hasan Serhan Yavuz, Hakan Çevikalp .A wavelet Tour of Signal ProcessingIEEE International Conference on signal processing june 2014,34:19-445.
  4. Tiecheng Yu. The Development State of the Voice Identification. The Development communication world.2005,2:56-59.
  5. Dian RetnoAnggraini .The development of a voice recognition system based on Principal Component Analysis(PCA) and unsupervised learning algorithm.2012,4:35-58.
  6. Jiqing Han, Lei Zhang, Tieran Zheng. Voice Signals Processing[M].Beijing: Tsinghua University Press 2004,3:67-94.
  7. Remzi Serdar Kurcan, “Isolated word recognition from in-ear microphone data using hidden markov models (hmm)”, Master’s Thesis, 2006.
  8. Nikolai Shokhirev ,”Hidden Markov Models “, 2010.
  9. L.R. Rabiner, “A tutorial on Hidden Markov Models and selected applications in Speech Recognition”, Proceedings of the IEEE Journal, Feb 1989, Vol 77, Issue: 2.
  10. Suma Swamy, Manasa S, Mani Sharma, Nithya A.S, Roopa K.S and K.V Ramakrishnan, “An Improved Speech Recognition System”, LNICST Springer Journal, 2013.
  11. Lindasalwa Muda, Mumtaj Begam and Elamvazuthi.,”Voice Recognition Algorithms using Mel Frequency Cepstral Coefficient (MFCC) and DTW Techniques “,Journal of Computing, Volume 2, Issue 3, March 2010.
  12. Mahdi Shaneh and Azizollah Taheri ,”Voice Command Recognition System based on MFCC and VQAlgorithms”, World Academy of Science, Engineering and Technology Journal , 2009.
  13. Remzi Serdar Kurcan, “Isolated word recognition from in-ear microphone data using hidden markovmodels (hmm)”, Master’s Thesis, 2006.
  14. Nikolai Shokhirev ,”Hidden Markov Models “, 2010.
  15. L.R. Rabiner, “A tutorial on Hidden Markov Models and selected applications in Speech Recognition”, Proceedings of the IEEE Journal, Feb 1989, Vol 77, Issue: 2.
  16. Suma Swamy, Manasa S, Mani Sharma, Nithya A.S, Roopa K.S and K.V Ramakrishnan, “An Improved Speech Recognition System”, LNICST Springer Journal, 2013.
  17. M. L. Shire and B. Y. Chen, “Data-driven RASTA filters in reverberation,”in Proc. ICASSP’00, 2000, vol. 3, pp. 1627–1630.
  18. T. Takiguchi and Y. Ariki, “Robust feature extraction using kernel PCA,” in Proc. ICASSP’06, 2006, pp. 509–512.
  19. S. Furui, “Speaker-independent isolated word recognition using dynamic features of speech spectrum,” IEEE Trans. Acoust., Speech,Signal Process., vol. ASSP-34, no. 1, pp. 52–59, Feb. 1986.
  20. O. Ichikawa, T. Fukuda, R. Tachibana, and M. Nishimura, “Dynamic features in the linear domain for robust automatic speech recognition in a reverberant environment,” in Proc. Interspeech’09, 2009, pp. 44–47.
  21. M. Nakayama et al., “CENSREC-4: Development of evaluation framework for distant-talking speech recognition under reverberant environments,” in Proc. Interspeech’08, 2008, pp. 968–971.
  22. T. Nishiura et al., “Evaluation framework for distant-talking speech recognition under reverberant environments—Newest part of the CENSREC series-,” in Proc. LREC ’08, 2008.
Index Terms

Computer Science
Information Sciences

Keywords

Dynamic Time Wrapping (DTW) Mel Frequency Cepstral Coefficient (MFCC) Voice recognition.

Powered by PhDFocusTM