Binary Classification Performances of Emotion Classes for Turkish Emotional Speech

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Tarih

2015

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Yayıncı

Ieee

Erişim Hakkı

info:eu-repo/semantics/closedAccess

Özet

Emotion recognition from speech plays important role for natural human-computer interaction. This study investigates binary classification performances of 4 fundamental emotion classes in Turkish Emotional Speech (TurES) Database using acoustic features for various classifiers. Results shows that Angry emotion class has higher classification rate (70%-80%) than others; lowest classification rate is obtained as 64% for Happy-Neutral emotion pair. Best classification results are obtained with J48 (C4.5) classifier for all emotion pairs.

Açıklama

23nd Signal Processing and Communications Applications Conference (SIU) -- MAY 16-19, 2015 -- Inonu Univ, Malatya, TURKEY

Anahtar Kelimeler

binary classification, emotional speech, categorical classification, TurES database, pattern recognition

Kaynak

2015 23rd Signal Processing and Communications Applications Conference (Siu)

WoS Q Değeri

N/A

Scopus Q Değeri

N/A

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