RANSAC-based training data selection for speaker state recognition

Name: RANSAC-based training data selection for speaker state recognition
Author: Bozkurt, E., Erzin, E., Erdem, Ç. E., Erdem, Tanju

İsim	RANSAC-based training data selection for speaker state recognition
Yazar	Bozkurt, E., Erzin, E., Erdem, Ç. E., Erdem, Tanju
Basım Tarihi:	2011
Basım Yeri	- The International Speech Communications Association
Konu	Speaker state challenge, Intoxication, Sleepiness, Ransac
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-1-61839-270-1
Kayıt Numarası	5da6cd17-531b-4308-b689-ede0b95c98b3
Lokasyon	Computer Science
Tarih	2011
Notlar	TÜBİTAK ; Türk Telekom
Örnek Metin	We present a Random Sampling Consensus (RANSAC) based training approach for the problem of speaker state recognition from spontaneous speech. Our system is trained and tested with the INTERSPEECH 2011 Speaker State Challenge corpora that includes the Intoxication and the Sleepiness Subchallenges, where each sub-challenge defines a two-class classification task. We aim to perform a RANSAC-based training data selection coupled with the Support Vector Machine (SVM) based classification to prune possible outliers, which exist in the training data. Our experimental evaluations indicate that utilization of RANSAC-based training data selection provides 66.32 % and 65.38 % unweighted average (UA) recall rate on the development and test sets for the Sleepiness Sub-challenge, respectively and a slight improvement on the Intoxicationubchallenge performance.

Kaynağa git Özyeğin Üniversitesi

Aramaya Dön

Özyeğin Üniversitesi

Kaynağa git

RANSAC-based training data selection for speaker state recognition

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

Basım Tarihi 2011

Basım Yeri - The International Speech Communications Association

Konu Speaker state challenge, Intoxication, Sleepiness, Ransac

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-1-61839-270-1

Kayıt Numarası 5da6cd17-531b-4308-b689-ede0b95c98b3

Lokasyon Computer Science

Tarih 2011

Notlar TÜBİTAK ; Türk Telekom

Örnek Metin We present a Random Sampling Consensus (RANSAC) based training approach for the problem of speaker state recognition from spontaneous speech. Our system is trained and tested with the INTERSPEECH 2011 Speaker State Challenge corpora that includes the Intoxication and the Sleepiness Subchallenges, where each sub-challenge defines a two-class classification task. We aim to perform a RANSAC-based training data selection coupled with the Support Vector Machine (SVM) based classification to prune possible outliers, which exist in the training data. Our experimental evaluations indicate that utilization of RANSAC-based training data selection provides 66.32 % and 65.38 % unweighted average (UA) recall rate on the development and test sets for the Sleepiness Sub-challenge, respectively and a slight improvement on the Intoxicationubchallenge performance.