CFP last date
20 January 2025
Reseach Article

Effect of Varying MFCC Filters for Speaker Recognition

by Amol A. Chaudhari, S.B. Dhonde
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 128 - Number 14
Year of Publication: 2015
Authors: Amol A. Chaudhari, S.B. Dhonde
10.5120/ijca2015906703

Amol A. Chaudhari, S.B. Dhonde . Effect of Varying MFCC Filters for Speaker Recognition. International Journal of Computer Applications. 128, 14 ( October 2015), 7-9. DOI=10.5120/ijca2015906703

@article{ 10.5120/ijca2015906703,
author = { Amol A. Chaudhari, S.B. Dhonde },
title = { Effect of Varying MFCC Filters for Speaker Recognition },
journal = { International Journal of Computer Applications },
issue_date = { October 2015 },
volume = { 128 },
number = { 14 },
month = { October },
year = { 2015 },
issn = { 0975-8887 },
pages = { 7-9 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume128/number14/22939-2015906703/ },
doi = { 10.5120/ijca2015906703 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:21:37.538935+05:30
%A Amol A. Chaudhari
%A S.B. Dhonde
%T Effect of Varying MFCC Filters for Speaker Recognition
%J International Journal of Computer Applications
%@ 0975-8887
%V 128
%N 14
%P 7-9
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents speaker recognition system with emphasis on MFCC feature extraction scheme. The optimum number of MFCC filter selection is necessary for the performance of speaker recognition. In this paper, the number of MFCC filters are varied. The effect of varying filters on computational time required for training and testing phase is provided in this paper. The experimental results have been evaluated on the developed database of 75 speakers. The recognition rate achieved is 96% in case of 30 MFCC filters with approximate computational time (testing phase) of 79.66 seconds.

References
  1. Douglas A. Reynolds, “An over view of automatic speaker recognition technology”, Acoustics, Speech, and Signal Processing (ICASSP), IEEE International Conference, vol. 4, 2002.
  2. Tomi Kinnunen, Haizhou Li, “An overview of text-independent speaker recognition: From features to supervectors”, Journal on Speech Communication, Elsevier, vol. 52, no. 1, pp. 12–40, 2010.
  3. Pawan K. Ajmera, Dattatray V. Jadhav, Ragunath S. Holambe, “Text-independent speaker identification using Radon and discrete cosine transforms based features from speech spectrogram”, Journal on Pattern Recognition, Elsevier, vol. 44, no. 10-11, pp. 2749-2759, 2011.
  4. Jian-Da Wu, Bing-Fu Lin, “Speaker identification using discrete wavelet packet transform technique with irregular decomposition”, Journal on Expert Systems with Applications, Elsevier, vol. 36, no. 2, pp. 3136–3143, 2009.
  5. Mangesh S. Deshpande, Raghunath S. Holambe, “New Filter Structure based Admissible Wavelet Packet Transform for Text-Independent Speaker Identification”, International Journal of Recent Trends in Engineering, vol. 2, no. 5, pp. 121-125, 2009.
  6. R.Shantha Selva Kumari, S. Selva Nidhyananthan, Anand.G, “Fused Mel Feature sets based Text-Independent Speaker Identification using Gaussian Mixture Model”, International Conference on Communication Technology and System Design 2011, Journal on Procedia Engineering, Elsevier, vol. 30, pp. 319–326, 2012.
  7. R. Rajeshwara Rao, A. Prasad, Ch. Kedari Rao, “Robust Features for Automatic Text-Independent Speaker Recognition Using Gaussian Mixture Model”, International Journal of Soft Computing and Engineering, vol. 1, Issue 5, November 2011.
  8. Noor Almaadeed, Amar Aggoun, Abbes Amira, “Speaker identification using multimodal neural networks and wavelet analysis”, IET Journals and Magazines, vol. 4, no. 1, pp. 18-28, 2015.
  9. Ahmad, K.S.; Thosar, A.S.; Nirmal, J.H.; Pande, V.S., "A unique approach in text independent speaker recognition using MFCC feature sets and probabilistic neural network," Advances in Pattern Recognition (ICAPR), 2015, pp. 1- 6, January 2015.
  10. M.Hassan Shirali-Shahreza, Sajad Shirali-Shahreza, “Effect of MFCC Normalization on Vector Quantization Based Speaker Identification”, Signal Processing and Information Technology (ISSPIT), 2010, pp.250,253, December 2010.
  11. Md Jahangir Alam , Tomi Kinnunen , Patrick Kenny , Pierre Ouellet, Douglas O’Shaughnessy, “Multitaper MFCC and PLP features for speaker verification using i-vectors”, Journal on Speech Communication, Elsevier, vol. 55, no. 2, pp. 237-251, 2013.
  12. Holambe, Raghunath S., Deshpande, Mangesh S., “Advances in Non-Linear Modeling for Speech Processing”, SpringerBriefs in Speech Technology, Section 6, pp. 77-82, ISBN 978-1-4614-1505-3, 2012.
Index Terms

Computer Science
Information Sciences

Keywords

Speaker Recognition Feature extraction MFCC LBG algorithm Euclidean distance