Ses verilerinden cinsiyet tespiti için yeni bir yaklaşım: Optimizasyon yöntemleri ile özellik seçimi

Year 2023, Volume: 38 Issue: 2, 1179 - 1192, 07.10.2022

Feyza Altunbey Özbay Erdal Özbay

https://doi.org/10.17341/gazimmfd.938294

Cited By: 1

Abstract

Son yıllarda, birçok farklı uygulama alanına sahip cinsiyet tespiti, konuşma analizinin önemli bir problemidir. Cinsiyet tespiti için perde, medyan, frekans gibi ses verilerinin farklı özelliklerinden yararlanılmaktadır. Bu çalışmada, ses verilerinden cinsiyet tespiti için metasezgisel optimizasyon algoritmalarını temel alan özellik seçimi yöntemi önerilmiştir. Önerilen yöntemde, ses verilerini en uygun biçimde temsil edecek özellik kümesi optimizasyon algoritmaları ile seçilmiş ve elde edilen özellikler kullanılarak yapay zekâ algoritmaları ile cinsiyet tespiti yapılmıştır. Ses verilerinden özellik seçimi yapmak için karmaşık problemleri çözmek konusunda yeteneklere sahip doğadan esinlenmiş metasezgisel optimizasyon algoritmaları kullanılmıştır. Parçacık Sürüsü Optimizasyonu (PSO), Karınca Koloni Optimizayonu (KKO), Salp Sürüsü Algoritması (SSA) ve Balina Optimizasyonu Algoritması (BOA) ses verilerinden özellik seçimi için ilk kez modellenmiştir. Metasezgisel optimizasyon algoritmalarının etkinliğini ölçmek için genel erişime açık veri kümesi kullanılmıştır. PSO, KKO, SSA ve BOA’nın özellik seçimi için performansları uygunluk fonksiyonu değeri, doğruluk değeri ve seçilen özellik sayısı olmak üzere üç farklı ölçüt bakımından karşılaştırılmıştır. Metasezgisel optimizasyon algoritmaları ile özellik seçimi yapıldıktan sonra elde edilen yeni veri kümeleri ve orijinal veri kümesine Naive Bayes ve Karar Ağacı algoritmaları uygulanmıştır. Yapılan analizler sonucunda, metasezgisel optimizasyon algoritmalarını özellik seçimi için kullanan bu yöntem sayesinde Naive Bayes ve Karar Ağacı algoritmaları ile elde edilen sonuçlarda başarı oranın arttığı gözlemlenmiştir.

Keywords

Cinsiyet tespiti, özellik seçimi, optimizasyon, sınıflandırma

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