Performance of svm, k-nn and nbc classifiers for text-independent speaker identification with and without modelling through merging models

Year 2016, , 1 - 6, 01.04.2016

Yussouf Nahayo Seçkin Arı

https://doi.org/10.16984/saufenbilder.00295

Abstract

This paper proposes some methods of robust text-independent speaker identification based on Gaussian Mixture Model (GMM). We implemented a combination of GMM model with a set of classifiers such as Support Vector Machine (SVM), K-Nearest Neighbour (K-NN), and Naive Bayes Classifier (NBC). In order to improve the identification rate, we developed a combination of hybrid systems by using validation technique. The experiments were performed on the dialect DR1 of the TIMIT corpus. The results have showed a better performance for the developed technique compared to the individual techniques.

Keywords

GMM, SVM, KNN, NB, TIMIT

Birleşik modellemeli ve modellemesiz metin-bağımsız konuşmacı tanıma için SVM, K-NN ve NBC sınıflandırıcıların başarımı

Abstract

Bu çalışma Gaussian Mixture Model tabanlı metin-bağımsız konuşmacı tanıma yöntemleri sunar. GMM model ile Support Vector Machine, K-nearest Neighbour ve Naive Bayes sınıflandırıcı gibi sınıflandırıcıların kombinasyonu gerçekleştirilmiştir. Tanıma oranını iyileştirmek için, doğrulama yöntemi kullanarak hibrid sistemlerin kombinasyonunu geliştirdik. Deneyler TIMIT corpus’ un DR1 lehçesi üzerine yapılmıştır. Sonuçlar ayrı ayrı yöntemlerle karılaştırıldığında geliştirilen yöntemle daha iyi başarım göstermiştir.

Keywords

GMM, Combination, SVM, KNN, NB, TIMIT

References

• D. A. N. R.Amami, “An Empirical Comparison
• of SVM and Some Supervised Learning
• Algorithms for Vowel recognition”,
• International Journal of Intelligent Information
• Processing IJIIP, 2012.
• B.S. Atal, “Automatic Recognition of Speaker
• from Their Voices”, Proceedings of the IEEE,
• Vol. 64, No. 4, pp 460-475, 1976
• W. M. Campbell, D. E. Sturim, D. A. Reynolds,
• and A. Solomon off, “SVM based speaker
• verification using a GMM supervector kernel and
• Without modelling GMM
• Classifiers SVM K-NN NB
• Identification rate
• (%)
• 87 92
• Y. Nahayo, S. Arı Performance of svm, k-nn and nbc classifiers for text
• independent speaker identification with and without
• modelling through merging models
• SAÜ Fen Bil Der 20. Cilt, 1. Sayı, s. 1-6, 2016
• NAP variability compensation”, Proc. Int. Conf.
• Acoustics, Speech, and Signal Processing, 2006.
• D. Reynolds and R. Rose, "Robust textindependent speaker identification using
• Gaussian mixture speaker models, " IEEE Trans.
• Speech Audio Proc., vol. 3, no. 1, pp. 72–83,
• -
• D. Ben Ayed Mezghani, S. Zribi Boujelbene et
• N. Ellouze, "Evaluation of SVM kernels function
• and conventional machine learning algorithms
• for Speaker Identification Task," International
• Journal of Hybrid Information Technology
• (IJHIT), vol. 3, pp. 2 3-34, 2010.
• S. Zribi Boujelbene, D. Ben Ayed Mezghan et N.
• Ellouze, "Application of Combining Classifiers
• for Text-Independent Speaker Identification,"
• the 16th IEEE International Conference on
• Electronics, Circuits, and Systems ICECS,
• Hammamet-Tunisie, pp. 723-726, 2009.
• R. Djemili, M. Bedda and H. Bourouba, "A
• Hybrid GMM/SVM System for Text
• Independent Speaker Identification,"
• International Journal of Computer and
• Information Science and Engineering, vol. 1, pp.
• -8, 2007.
• D. Neiberg “Text Independent Speaker
• Verification Using Adapted Gaussian Mixture
• Models", Centre for Speech Technology (CTT)
• Department of Speech, Music and Hearing KTH,
• Stockholm, Sweden 2001-12-11.
• S. Zribi Boujelbene, D. Ben Ayed Mezghan et N.
• Ellouze, “Support Vector Machines approaches
• and its application to speaker identification," 3rd
• IEEE International Conference on Digital
• Ecosystems and Technologies DEST, pp. 662-
• , 2009.
• I. Ayed, “Stratégies de fusion de paramètres pour
• une tâche d'identification du locuteur en mode
• indépendant du texte : Application sur le corpus
• NTIMIT.TAIMA”, Hammamet-Tunisie 2011.
• L. Lam et C.Y. Suen. “Application of Majority
• Voting to Pattern Recognition: An Analysis of Its
• Behavior and Performance”, IEEE Transactions
• on Systems, Man Cybernetics, pp. 553-568,
• -
• K. S. Durgesh, and B. Lekha, “Data classification
• using support vector machine.” Journal of
• Theoretical and Applied Information
• Technology, 12(1), 1-7, 2010
• H. Y. Chang, A. L. Kong, and L. Haizhou, “An
• SVM Kernel With GMM-Supervector Based on
• the Bhattacharyya Distance for Speaker
• Recognition”, v.6, pp. 1300-1312, 2010