Improving automatic emotion recognition from speech signals

Name: Improving automatic emotion recognition from speech signals
Author: Bozkurt, E., Erzin, E., Eroğlu Erdem, Ç., Erdem, Tanju

İsim	Improving automatic emotion recognition from speech signals
Yazar	Bozkurt, E., Erzin, E., Eroğlu Erdem, Ç., Erdem, Tanju
Basım Tarihi:	2009
Basım Yeri	- International Speech Communications Association
Konu	Emotion recognition, Prosody modeling
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-1-61567-692-7
Kayıt Numarası	eab8cbae-d3f4-4678-84b4-9930c63ca4ba
Lokasyon	Computer Science
Tarih	2009
Notlar	TÜBİTAK
Örnek Metin	We present a speech signal driven emotion recognition system. Our system is trained and tested with the INTERSPEECH 2009 Emotion Challenge corpus, which includes spontaneous and emotionally rich recordings. The challenge includes classifier and feature sub-challenges with five-class and two-class classification problems. We investigate prosody related, spectral and HMM-based features for the evaluation of emotion recognition with Gaussian mixture model (GMM) based classifiers. Spectral features consist of mel-scale cepstral coefficients (MFCC), line spectral frequency (LSF) features and their derivatives, whereas prosody-related features consist of mean normalized values of pitch, first derivative of pitch and intensity. Unsupervised training of HMM structures are employed to define prosody related temporal features for the emotion recognition problem. We also investigate data fusion of different features and decision fusion of different classifiers, which are not well studied for emotion recognition framework. Experimental results of automatic emotion recognition with the INTERSPEECH 2009 Emotion Challenge corpus are presented.

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Özyeğin Üniversitesi

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Improving automatic emotion recognition from speech signals

Yazar Bozkurt, E., Erzin, E., Eroğlu Erdem, Ç., Erdem, Tanju

Basım Tarihi 2009

Basım Yeri - International Speech Communications Association

Konu Emotion recognition, Prosody modeling

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-1-61567-692-7

Kayıt Numarası eab8cbae-d3f4-4678-84b4-9930c63ca4ba

Lokasyon Computer Science

Tarih 2009

Notlar TÜBİTAK

Örnek Metin We present a speech signal driven emotion recognition system. Our system is trained and tested with the INTERSPEECH 2009 Emotion Challenge corpus, which includes spontaneous and emotionally rich recordings. The challenge includes classifier and feature sub-challenges with five-class and two-class classification problems. We investigate prosody related, spectral and HMM-based features for the evaluation of emotion recognition with Gaussian mixture model (GMM) based classifiers. Spectral features consist of mel-scale cepstral coefficients (MFCC), line spectral frequency (LSF) features and their derivatives, whereas prosody-related features consist of mean normalized values of pitch, first derivative of pitch and intensity. Unsupervised training of HMM structures are employed to define prosody related temporal features for the emotion recognition problem. We also investigate data fusion of different features and decision fusion of different classifiers, which are not well studied for emotion recognition framework. Experimental results of automatic emotion recognition with the INTERSPEECH 2009 Emotion Challenge corpus are presented.