Extraction of novel features based on histograms of MFCCs used in emotion classification from generated original speech dataset

Name: Extraction of novel features based on histograms of MFCCs used in emotion classification from generated original speech dataset
Author: Pakyurek, M., Atmış, Mahir, Kulac, S., Uludag, U.

İsim	Extraction of novel features based on histograms of MFCCs used in emotion classification from generated original speech dataset
Yazar	Pakyurek, M., Atmış, Mahir, Kulac, S., Uludag, U.
Basım Tarihi:	2020
Konu	Emotion classification, MFCC, SVM, Speech signal
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1392-1215
Kayıt Numarası	26c02f4a-d379-4a77-9f08-69d6e8915ade
Tarih	2020
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.26.1.25309
Cilt	26

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Extraction of novel features based on histograms of MFCCs used in emotion classification from generated original speech dataset

Yazar Pakyurek, M., Atmış, Mahir, Kulac, S., Uludag, U.

Basım Tarihi 2020

Konu Emotion classification, MFCC, SVM, Speech signal

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1392-1215

Kayıt Numarası 26c02f4a-d379-4a77-9f08-69d6e8915ade

Tarih 2020

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.26.1.25309

Cilt 26