Konuşmacıları Kadın, Erkek ve Çocuk Olarak Sınıflandırmada Veri Artırmanın Performansa Etkisi

Year 2024, Volume: 14 Issue: 3, 974 - 987, 01.09.2024

Ergün Yücesoy

https://doi.org/10.21597/jist.1505349

Abstract

Derin öğrenme alanındaki gelişmeler daha doğru sınıflandırıcıların oluşturulmasına olanak sağlamıştır. Ancak yüksek genelleme yeteneğine sahip derin öğrenme modellerinin oluşturulabilmesi için büyük miktarda etiketli veri kümelerine ihtiyaç duyulmaktadır. Veri artırma bu ihtiyacın karşılanmasında yaygın olarak kullanılan bir yöntemdir. Bu çalışmada konuşmacıların yaş ve cinsiyetlerine göre sınıflandırılmasında farklı veri artırma yöntemlerinin sınıflandırma performansı üzerindeki etkileri araştırılmıştır. Çalışmada yetişkin konuşmacılar erkek ve kadın olarak, çocuklar ise cinsiyet ayrımı yapılmadan tek bir sınıf olarak değerlendirilmiş ve toplamda üç (kadın, erkek ve çocuk) sınıflı bir sınıflandırma gerçekleştirilmiştir. Bu amaç doğrultusunda gürültü ekleme, zaman uzatma ve perde kaydırma olmak üzere üç veri artırma yöntemi farklı kombinasyonlarda kullanılarak yedi farklı model oluşturulmuş ve her birinin performans ölçümleri yapılmıştır. aGender veri kümesinden rastgele seçilen 5760 konuşma verisi ile geliştirilen bu modeller arasında en yüksek performans artışı üç veri artırma yönteminin birlikte kullanıldığı modelle sağlanmıştır. Bu model sınıflandırma doğruluğunu %84.583’den % 87.523’e çıkararak %3’e yakın performans artışı sağlarken veri artırmanın kullanıldığı diğer modellerde de %1 ile %2.3 arasında performans artışı sağlanmıştır.

Keywords

Yaş ve cinsiyet Tanıma, Evrişimli sinir ağları, Veri artırma, Perde kaydırma, Zaman uzatma, Gürültü ekleme

Effect of Data Augmentation on Performance in Classifying Speakers as Female, Male, and Child

Abstract

Developments in the field of deep learning have enabled the creation of more accurate classifiers. However, large amounts of labeled datasets are needed to create deep learning models with high generalization ability. Data augmentation is a widely used method to address the need for more data. This study investigates the effects of different data augmentation methods on the classification performance of speakers based on their age and gender. In this study, adult speakers are classified as male or female, while children are classified as a single group without gender discrimination, resulting in a total of three classes (female, male, and child). For this purpose, seven different models are created using combinations of three data augmentation methods: noise addition, time stretching, and pitch shifting. The performance of each model is then evaluated. Among these models, which were developed with 5760 speech data randomly selected from the aGender dataset, the highest performance increase is achieved with the model where three data augmentation methods are used together. This model increases the classification accuracy from 84.583% to 87.523%, providing a performance increase of nearly 3%, while other models using data augmentation provide a performance increase of 1% to 2.3%.

Keywords

Age and gender recognition, Convolutional neural networks, Data augmentation, Pitch shift, Time stretching Noise addition

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