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

Yussouf Nahayo; Seçkin Arı

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

Article Details

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

Authors:

Yussouf Nahayo | Seçkin Arı

Main Topics:

Applied physics

Journal:

Sakarya University Journal of Science 2016 Vol.20 Issue 1, pp.1-6

Document Type:

Article

Publication Date:

3/2016

Document Language:

English

Document ID (Org No.):

2000558672

DOI:

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.

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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.

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Article Details

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

Authors: Yussouf Nahayo | Seçkin Arı

Main Topics: Applied physics

Journal: Sakarya University Journal of Science 2016 Vol.20 Issue 1, pp.1-6

Document Type: Article

Publication Date: 3/2016

Document Language: English

Document ID (Org No.): 2000558672

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

Issues

Authors:

Yussouf Nahayo | Seçkin Arı

Main Topics:

Applied physics

Journal:

Sakarya University Journal of Science 2016 Vol.20 Issue 1, pp.1-6

Document Type:

Article

Publication Date:

3/2016

Document Language:

English

Document ID (Org No.):

2000558672

DOI:

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