Türkçe Sesli Komut Verilerinin Evrişimsel Sinir Ağı ile Sınıflandırılması

Yıl 2025, Cilt: 7 Sayı: 1, 51 - 59, 30.04.2025

Betül Karakaş

https://doi.org/10.46387/bjesr.1581590

Öz

Bu çalışma kapsamında derin öğrenme literatüründe yaygın olarak kullanılan yöntemlerden biri olan evrişimsel sinir ağları kullanılarak Türkçe Sesli Komut Veri Kümesi üzerinde sınıflandırma işlemi gerçekleştirilmiştir. Toplamda 849.57 MB dosya boyutu bulunan 14 farklı komut barındıran ve 1 saniyelik 26.485 ses dosyasından oluşan bu veri kümesinin asıl amacı küresel olarak kullanılan İngilizce bir sesli komut veri kümesi bulunurken belirli bir görev için literatürde Türkçe sesli komut veri kümesinin bulunmaması ve yaygınlaştırılmasıdır. Performans parametresi olarak “Doğruluk” kullanılmıştır. Bir evrişimsel sinir ağı mimarisi olan YAMNET ağı, toplamda 2 koşum olarak minimum 128 küme büyüklüğü için her bir koşumda 165 iterasyon için eğitilmiştir. Mimarinin test işlemi her bir komut için incelenmiş ve YamNet mimarisi yüksek performans göstermiştir. Eğitim sonunda %98,04 validasyon doğruluğu elde edilmiş ve eğitim süreci boyunca ağın hiç görmediği veriler ile ortalama %97,44 test doğruluğuna ulaşılmıştır.

Anahtar Kelimeler

Derin Öğrenme , Türkçe Sesli Komut , Evrişimsel Sinir Ağları , Yamnet , Matlab

Classification of Turkish Voice Command Data with Convolutional Neural Network

Öz

In this study, the classification process was performed on the Turkish Voice Command Dataset using convolutional neural networks (CNN), one of the widely used methods in the deep-learning literature. The main purpose of this dataset, which consists of 26,485 1-second audio files containing 14 different commands with a total file size of 849.57 MB, is to popularize the fact that there is a globally used English voice command dataset, but there is no Turkish voice command dataset in the literature for a specific task. "Accuracy" was used as the performance metric. A CNN architecture, YamNet was trained for a total of 2 epochs, with 165 iterations in each run for 128 minibatch size. Testing of architecture process was examined for each command and it was revealed that YamNet classified with high performance. At the end of the training, 98.04% validation accuracy obtained and 97.44% average test accuracy were achieved with data that the network had never seen during its training phase.

Anahtar Kelimeler

Deep Learning , Turkish Voice Command , Convolutional Neural Networks , Yamnet , Matlab

Kaynakça

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