Title: Recognition of Turkish Command to Play Chess Game Using CNN

Abstract: A platform has been created that allows playing chess with Turkish voice commands. The aim of this study is to enable individuals with limited movement abilities as a result of congenital reasons or a certain disease or accident to play chess and perform a social activity without the help of another person, and to be rehabilitated at the same time. It consists of three parts: Chess module, Human-computer interaction module and Artificial Intelligence module. 29 words have been determined to provide movement in the game on the platform. Voice recordings from 151 people, 86 men and 65 women, were used. Feature selection was made on 43790 voice recordings by using mel frequency cepstral coefficients (MFCC) and gammatone cepstral coefficients (GTCC) methods. The results obtained were classified using the traditional CNN model. The data obtained after using MFCC and GTCC methods were used as inputs in the CNN model. In addition, the data obtained by the two methods were combined and trained in the model. Depending on the methods used in the created model, 83% to 85.9% results were obtained. It was determined that the results obtained using the MFCC method were more successful.

Keywords: Chess; MFCC; GTCC; deep learning; human-computer interaction

Başlık: Satranç Oyunu için CNN Kullanılarak Türkçe Komutları Tanıma

Özet: Türkçe ses komutları ile satranç oynanmasını sağlayan bir platform oluşturulmuştur. Bu çalışmanın amacı doğuştan, belirli bir hastalık ya da kaza sonucu hareket yetenekleri kıstlanmış bireylerin sosyal bir etkinlik olarak satranç oynamalarını ve başka bir kişinin yardımı olmadan sosyal bir aktivite gerçekleştirebilmelerini ve bir yandan da rehabilite olmalarını sağlamaktır. Satranç modülü, İnsan bilgisayar etkileşim modülü ve Yapay Zeka modülü olmak üzere üç bölümden oluşmaktadır. Platform üzerinde oyun içinde hareketin sağlanması için 29 sözcük belirlenmiştir. 86 erkek, 65 kadın olmak üzere 151 kişiden alınan ses kayıtları kullanılmıştır. 43790 ses kaydı üzerinde mel frekanslı cepstral katsayıları (MFCC) ve gammatone cepstral katsayıları (GTCC) yöntemleri kullanılarak öznitelik seçilimi yapılmıştır. Elde edilen sonuçlar geleneksel CNN modeli ile sınıflandırma işlemi yapılmıştır. CNN modelinde girdi olarak MFCC ve GTCC yöntemleri kullanıldıktan sonra elde edilen veriler kullanılmıştır. Ayrıca iki yöntem ile elde edilen veriler birleştirilerek model içinde eğitime alınmıştır. Oluşturulan model de kullanılan yöntemlere bağlı olarak %83 ile %85,9 sonuç elde edilmiştir. MFCC yöntemi kullanılarak elde edilen sonuçların daha başarılı olduğu belirlenmiştir.

Anahtar kelimeler: Satranç; MFCC; GTCC; derin öğrenme; insan-bilgisayar etkileşimi

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