RANSAC-based training data selection on spectral features for emotion recognition from spontaneous speech

Name: RANSAC-based training data selection on spectral features for emotion recognition from spontaneous speech
Author: Bozkurt, E., Erzin, E., Erdem, Tanju, Eroğlu Erdem, Ç.

İsim	RANSAC-based training data selection on spectral features for emotion recognition from spontaneous speech
Yazar	Bozkurt, E., Erzin, E., Erdem, Tanju, Eroğlu Erdem, Ç.
Basım Tarihi:	2011
Basım Yeri	- Springer International Publishing
Konu	Affect recognition, Emotional speech classification, RANSAC, Data cleaning, Decision fusion
Tür	Kitap
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	0302-9743
Kayıt Numarası	15a4496b-b117-4f1e-8eb7-a0d2157fe788
Lokasyon	Computer Science
Tarih	2011
Notlar	TÜBİTAK
Örnek Metin	Training datasets containing spontaneous emotional speech are often imperfect due the ambiguities and difficulties of labeling such data by human observers. In this paper, we present a Random Sampling Consensus (RANSAC) based training approach for the problem of emotion recognition from spontaneous speech recordings. Our motivation is to insert a data cleaning process to the training phase of the Hidden Markov Models (HMMs) for the purpose of removing some suspicious instances of labels that may exist in the training dataset. Our experiments using HMMs with Mel Frequency Cepstral Coefficients (MFCC) and Line Spectral Frequency (LSF) features indicate that utilization of RANSAC in the training phase provides an improvement in the unweighted recall rates on the test set. Experimental studies performed over the FAU Aibo Emotion Corpus demonstrate that decision fusion configurations with LSF and MFCC based classifiers provide further significant performance improvements.
DOI	10.1007/978-3-642-25775-9_3
Cilt	6800

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RANSAC-based training data selection on spectral features for emotion recognition from spontaneous speech

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

Basım Tarihi 2011

Basım Yeri - Springer International Publishing

Konu Affect recognition, Emotional speech classification, RANSAC, Data cleaning, Decision fusion

Tür Kitap

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 0302-9743

Kayıt Numarası 15a4496b-b117-4f1e-8eb7-a0d2157fe788

Lokasyon Computer Science

Tarih 2011

Notlar TÜBİTAK

Örnek Metin Training datasets containing spontaneous emotional speech are often imperfect due the ambiguities and difficulties of labeling such data by human observers. In this paper, we present a Random Sampling Consensus (RANSAC) based training approach for the problem of emotion recognition from spontaneous speech recordings. Our motivation is to insert a data cleaning process to the training phase of the Hidden Markov Models (HMMs) for the purpose of removing some suspicious instances of labels that may exist in the training dataset. Our experiments using HMMs with Mel Frequency Cepstral Coefficients (MFCC) and Line Spectral Frequency (LSF) features indicate that utilization of RANSAC in the training phase provides an improvement in the unweighted recall rates on the test set. Experimental studies performed over the FAU Aibo Emotion Corpus demonstrate that decision fusion configurations with LSF and MFCC based classifiers provide further significant performance improvements.

DOI 10.1007/978-3-642-25775-9_3

Cilt 6800