Yazar
Pakyurek, M., Atmış, Mahir, Kulac, S., Uludag, U.
Basım Tarihi
2020-02-17
Basım Yeri
-
Kauno Technologijos Universitetas
Konu
Emotion classification, MFCC, Speech signal, SVM
Tür
Süreli Yayın
Dil
İngilizce
Dijital
Evet
Yazma
Hayır
Kütüphane
Özyeğin Üniversitesi
Demirbaş Numarası
1392-1215
Kayıt Numarası
402ed8a8-d474-4596-9997-6da02b63c12b
Tarih
2020-02-17
Örnek Metin
This paper introduces two significant contributions: one is a new feature based on histograms of MFCC (Mel-Frequency Cepstral Coefficients) extracted from the audio files that can be used in emotion classification from speech signals, and the other – our new multi-lingual and multi-personal speech database, which has three emotions. In this study, Berlin Database (BD) (in German) and our custom PAU database (in English) created from YouTube videos and popular TV shows are employed to train and evaluate the test results. Experimental results show that our proposed features lead to better classification of results than the current state-of-the-art approaches with Support Vector Machine (SVM) from the literature. Thanks to our novel feature, this study can outperform a number of MFCC features and SVM classifier based studies, including recent researches. Due to the lack of our novel feature based approaches, one of the most common MFCC and SVM framework is implemented and one of the most common database Berlin DB is used to compare our novel approach with these kind of approaches.
DOI
10.5755/j01.eie.26.1.25310
Cilt
26
