Use of line spectral frequencies for emotion recognition from speech

Name: Use of line spectral frequencies for emotion recognition from speech
Author: Bozkurt, E., Erzin, E., Eroğlu Erdem, Ç., Erdem, Tanju

İsim	Use of line spectral frequencies for emotion recognition from speech
Yazar	Bozkurt, E., Erzin, E., Eroğlu Erdem, Ç., Erdem, Tanju
Basım Tarihi:	2010
Basım Yeri	- IEEE
Konu	Gaussian processes, Emotion recognition, Signal classification, Signal representation, Speech recognition
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1051-4651
Kayıt Numarası	fd611d87-0f31-4d6e-aa01-7f1ef8e66f9d
Lokasyon	Computer Science
Tarih	2010
Notlar	TUBİTAK ; Bahçeşehir University Research Fund
Örnek Metin	We propose the use of the line spectral frequency (LSF) features for emotion recognition from speech, which have not been been previously employed for emotion recognition to the best of our knowledge. Spectral features such as mel-scaled cepstral coefficients have already been successfully used for the parameterization of speech signals for emotion recognition. The LSF features also offer a spectral representation for speech, moreover they carry intrinsic information on the formant structure as well, which are related to the emotional state of the speaker. We use the Gaussian mixture model (GMM) classifier architecture, that captures the static color of the spectral features. Experimental studies performed over the Berlin Emotional Speech Database and the FAU Aibo Emotion Corpus demonstrate that decision fusion configurations with LSF features bring a consistent improvement over the MFCC based emotion classification rates.
DOI	10.1109/ICPR.2010.903

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Use of line spectral frequencies for emotion recognition from speech

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

Basım Tarihi 2010

Basım Yeri - IEEE

Konu Gaussian processes, Emotion recognition, Signal classification, Signal representation, Speech recognition

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1051-4651

Kayıt Numarası fd611d87-0f31-4d6e-aa01-7f1ef8e66f9d

Lokasyon Computer Science

Tarih 2010

Notlar TUBİTAK ; Bahçeşehir University Research Fund

Örnek Metin We propose the use of the line spectral frequency (LSF) features for emotion recognition from speech, which have not been been previously employed for emotion recognition to the best of our knowledge. Spectral features such as mel-scaled cepstral coefficients have already been successfully used for the parameterization of speech signals for emotion recognition. The LSF features also offer a spectral representation for speech, moreover they carry intrinsic information on the formant structure as well, which are related to the emotional state of the speaker. We use the Gaussian mixture model (GMM) classifier architecture, that captures the static color of the spectral features. Experimental studies performed over the Berlin Emotional Speech Database and the FAU Aibo Emotion Corpus demonstrate that decision fusion configurations with LSF features bring a consistent improvement over the MFCC based emotion classification rates.