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

Yıl 2016, , 1 - 6, 01.04.2016

Yussouf Nahayo Seçkin Arı

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

Öz

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.

Anahtar Kelimeler

GMM, SVM, KNN, NB, TIMIT

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

Öz

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.

Anahtar Kelimeler

GMM, Combination, SVM, KNN, NB, TIMIT

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