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IJMLC 2017 Vol.7(5): 133-138 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2017.7.5.635

Robust Speaker Identification Using Fusion of Features and Classifiers

Smarajit Bose, Amita Pal, Anish Mukherjee, and Debasmita Das

Abstract—Speaker identification using Gaussian Mixture Models (GMMs) based on Mel Frequency Cepstral Coefficients (MFCCs) as features, proposed by Reynolds (1995), is one of the most effective approaches available in the literature. The use of GMMs for modeling speaker identity is motivated by the interpretation that the Gaussian components represent some general speaker-dependent spectral shapes, and the capability of mixtures to model arbitrary densities. In this work, we have established empirically how combining two different well-known set of features (MFCCs and Perceptual Linear Predictive Coefficients) and using ensemble classifiers in conjunction with principal component transformation and some robust estimation procedures, can be used to enhance significantly the performance of the MFCC-GMM speaker recognition systems, using the benchmark speech corpus NTIMIT.

Index Terms—Mel frequency cepstral coefficients, Perceptual Linear Predictive Coefficients, Gaussian mixture models, ensemble classifiers, classification accuracy, trimmed means, NTIMIT.

Smarajit Bose and Amita Pal are with the Interdisciplinary Statistical Research Unit, Applied Statistics Division, Indian Statistical Institute, Kolkata, India (e-mail: {smarajit,pamita}@isical.ac.in).
Anish Mukherjee is with the Department of Statistics, University of Missouri, Colombia, MO, USA (e-mail: anishmk9@gmail.com).
Debasmita Das is with Department of Statistics, University of Connecticut, Storrs, CT, USA (e-mail: debasmita88@yahoo.com).

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Cite: Smarajit Bose, Amita Pal, Anish Mukherjee, and Debasmita Das, "Robust Speaker Identification Using Fusion of Features and Classifiers," International Journal of Machine Learning and Computing vol. 7, no. 5, pp. 133-138, 2017.

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net


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