EMOTION RECOGNITION STUDY BETWEEN DIFFERENT DATA SETS

Abstract

Speaking is the most important communication tool between people. By speaking people can transfer not only their thoughts but also their feelings to each other, too. When speaking we can estimate the conception, feeling, gender and age of the person we are talking to. In this study, a new sense of a data set called EmoSTAR is presented and cross tests with Berlin emotions data set is made. In the cross-test, one of the data set is used as training set while the other data set is used as test set. Additionally, in the study the performance of feature selector has been also examined. For feature extraction MFCC number is increased from 12 to 24 in openSMILE Emobase and Emo_large configurations and also developed by adding the Harmonic-to-Noise-Ratio features. The feature selection and classification is made by the Weka tool. EmoSTAR is currently under development for more emotion and sample type

Keywords

• Cross-corpus emotion analysis, Emotion mining, Prosody, EmoDB, EmoSTAR

FARKLI VERİ SETLERİ ARASINDA DUYGU TANIMA ÇALIŞMASI

Abstract

İnsanlar arasındaki en önemli iletişim aracı konuşmadır. Konuşma ile insanlar birbirlerine sadece düşüncelerini değil duygularını da aktarabilirler. Konuşma ile karşımızdaki kişinin düşüncesini, duygusunu, cinsiyetini ve yaşını da tahmin edebilir. Bu çalışmada EmoSTAR adlı yeni bir duygu veri seti sunulmuş ve Berlin Duygu Veri seti ile çapraz testler yapılmıştır. Çapraz testlerde, setlerden biri eğitim diğeri test seti olarak kullanılmıştır. Ayrıca, çalışmada özellik seçicilerin performansı da incelenmiştir. Özellik çıkarımı için openSMILE Emobase ve Emo_large konfigürasyonlarında MFCC sayısı 12’den 24’e çıkartılarak ve Harmonik Gürültü Oranı özellikleri eklenerek gerçekleştirilmiştir. Özellik seçme ve sınıflandırma ise Weka aracıyla yapılmıştır. EmoSTAR halen daha fazla duygu türü ve örnek için geliştirilme aşamasındadır.

Keywords

• Çok setli duygu analizi, Duygu madenciliği, Prozodi, EmoDB, EmoSTAR

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