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

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

İsim	RANSAC-based training data selection for emotion recognition from spontaneous speech
Yazar	Eroğlu Erdem, Ç., Bozkurt, E., Erzin, E., Erdem, Tanju
Basım Tarihi:	2010
Basım Yeri	- ACM
Konu	Affect recognition, Emotional speech classification, RANSAC, Data cleaning, Data pruning
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-1-4503-0170-1
Kayıt Numarası	cedcd21c-080b-4f64-93a2-a0d5e81dba52
Lokasyon	Computer Science
Tarih	2010
Notlar	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Örnek Metin	Training datasets containing spontaneous emotional expressions 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 various number of states and Gaussian mixtures per state indicate that utilization of RANSAC in the training phase provides an improvement of up to 2.84% in the unweighted recall rates on the test set. This improvement in the accuracy of the classifier is shown to be statistically significant using McNemar’s test.
DOI	10.1145/1877826.1877831

Kaynağa git Özyeğin Üniversitesi

Aramaya Dön

Özyeğin Üniversitesi

Kaynağa git

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

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

Basım Tarihi 2010

Basım Yeri - ACM

Konu Affect recognition, Emotional speech classification, RANSAC, Data cleaning, Data pruning

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-1-4503-0170-1

Kayıt Numarası cedcd21c-080b-4f64-93a2-a0d5e81dba52

Lokasyon Computer Science

Tarih 2010

Notlar Due to copyright restrictions, the access to the full text of this article is only available via subscription.

Örnek Metin Training datasets containing spontaneous emotional expressions 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 various number of states and Gaussian mixtures per state indicate that utilization of RANSAC in the training phase provides an improvement of up to 2.84% in the unweighted recall rates on the test set. This improvement in the accuracy of the classifier is shown to be statistically significant using McNemar’s test.

DOI 10.1145/1877826.1877831