Author
Pakyurek, M., Atmış, Mahir, Kulac, S., Uludag, U.
Publication Date
2020-02-17
Publication Place
-
Kauno Technologijos Universitetas
Subject
Emotion classification, MFCC, Speech signal, SVM
Type
Periodical
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
1392-1215
Record ID
402ed8a8-d474-4596-9997-6da02b63c12b
Date
2020-02-17
Sample Text
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
